Computer-aided Diagnostic System Research Articles

An efficient computational framework for gastrointestinal disorder prediction using attention-based transfer learning

Diagnosing gastrointestinal (GI) disorders, which affect parts of the digestive system such as the stomach and intestines, can be difficult even for experienced gastroenterologists due to the variety of ways these conditions present. Early diagnosis is critical for successful treatment, but the review process is time-consuming and labor-intensive. Computer-aided diagnostic (CAD) methods provide a solution by automating diagnosis, saving time, reducing workload, and lowering the likelihood of missing critical signs. In recent years, machine learning and deep learning approaches have been used to develop many CAD systems to address this issue. However, existing systems need to be improved for better safety and reliability on larger datasets before they can be used in medical diagnostics. In our study, we developed an effective CAD system for classifying eight types of GI images by combining transfer learning with an attention mechanism. Our experimental results show that ConvNeXt is an effective pre-trained network for feature extraction, and ConvNeXt+Attention (our proposed method) is a robust CAD system that outperforms other cutting-edge approaches. Our proposed method had an area under the receiver operating characteristic curve of 0.9997 and an area under the precision-recall curve of 0.9973, indicating excellent performance. The conclusion regarding the effectiveness of the system was also supported by the values of other evaluation metrics.

Skin Cancer Segmentation using CNN

This groundbreaking research introduces a comprehensive strategy for advancing medical image segmentation, merging two pivotal concepts to significantly enhance both the accuracy and efficiency of the segmentation process. The first component of our approach involves the integration of polar transformations as a preprocessing step applied to the original dataset. This transformative technique is designed to address the challenges associated with segmenting single structures of elliptical shape in medical images, such as organs (e.g., heart and kidneys), skin lesions, polyps, and various abnormalities. By centering the polar transformation on the object's focal point, a reduction in dimensionality is achieved, coupled with a distinct separation of segmentation and localization tasks. Two distinct methodologies for selecting an optimal polar origin are proposed: one involving estimation through a segmentation neural network trained on non-polar images, and the other employing a dedicated neural network trained to pre dict the optimal origin. The second key element of our approach is around the integration of the DoubleU-Net architecture, a powerful encoder-decoder model specifically designed for the task of semantic image segmentation. DoubleU- Net is a group of two U-Net architectures, each with a specific purpose. The initial U-Net is pre-trained on VGG-19 as the encoder and uses features learned from ImageNet to provide efficient information transfer. In order to store more semantic information and content, a second U-Net was added to the base to enhance the capabilities of the network. Join Atrous Spatial Pyramid Pooling (ASPP) to develop network data extraction content. The combination of DoubleU-Net architecture and joint transformation as a step forward shows good segmentation performance in different clinical tasks, including liver segmentation, polyp detection vision, skin segmentation, and epicardial fat tissue segmentation. It shows that various medical projects, including various diagnostic methods such as colonoscopy, dermoscopy, microscopy, have a positive impact on the plan. More importantly, the method performs well in difficult cases, such as the segmentation of small and flat polyps in CVC-ClinicDB and the 2015 subset of the MICCAI Automated Polyp Detection dataset. The results demonstrate the accuracy and generality of the combination, making it the best way to evaluate medical images in context; Our study has revealed a new method of skin cancer diagnosis that combines the power of deep learning with innovation. Advanced technology. The combination of dual U-Net architecture and polar coordinate transformation not only improves the accuracy of classification of lesions but also improves the robustness of the model to changes in image features. This study contributes to the development of computer-aided diagnostic systems for early diagnosis. Experimental results show that our method provides good accuracy, sensitivity, and specificity in detecting malignant and benign tumors. Additionally, we are conducting ablation studies to determine the contribution of each presentation and treatment of skin cancer to ultimately benefit patients and determine these benefits. We also apply the transformation of the joint as the first step to improve the discrimination ability of the model. This mechanical change effectively reduces the impact caused by changes in wound size, shape, and direction by displaying the original image of the polar system. By standardizing the representation of skin diseases, polar transformation improves the model's ability to generalize across different data sets and improves overall performance.

Computer-aided colorectal cancer diagnosis: AI-driven image segmentation and classification

Colorectal cancer is an enormous health concern since it is among the most lethal types of malignancy. The manual examination has its limitations, including subjectivity and data overload. To overcome these challenges, computer-aided diagnostic systems focusing on image segmentation and abnormality classification have been developed. This study presents a two-stage approach for the automatic detection of five types of colorectal abnormalities in addition to a control group: polyp, low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia, serrated adenoma, adenocarcinoma. In the first stage, UNet3+ was used for image segmentation to locate the anomalies, while in the second stage, the Cross-Attention Multi-Scale Vision Transformer deep learning model was used to predict the type of anomaly after highlighting the anomaly on the raw images. In anomaly segmentation, UNet3+ achieved values of 0.9872, 0.9422, 0.9832, and 0.9560 for Dice Coefficient, Jaccard Index, Sensitivity, Specificity respectively. In anomaly detection, the Cross-Attention Multi-Scale Vision Transformer model attained a classification performance of 0.9340, 0.9037, 0.9446, 0.8723, 0.9102, 0.9849 for accuracy, F1 score, precision, recall, Matthews correlation coefficient, and specificity, respectively. The proposed approach proves its capacity to alleviate the overwhelm of pathologists and enhance the accuracy of colorectal cancer diagnosis by achieving high performance in both the identification of anomalies and the segmentation of regions.

Lightweight Low-Rank Adaptation Vision Transformer Framework for Cervical Cancer Detection and Cervix Type Classification.

Cervical cancer is a major health concern worldwide, highlighting the urgent need for better early detection methods to improve outcomes for patients. In this study, we present a novel digital pathology classification approach that combines Low-Rank Adaptation (LoRA) with the Vision Transformer (ViT) model. This method is aimed at making cervix type classification more efficient through a deep learning classifier that does not require as much data. The key innovation is the use of LoRA, which allows for the effective training of the model with smaller datasets, making the most of the ability of ViT to represent visual information. This approach performs better than traditional Convolutional Neural Network (CNN) models, including Residual Networks (ResNets), especially when it comes to performance and the ability to generalize in situations where data are limited. Through thorough experiments and analysis on various dataset sizes, we found that our more streamlined classifier is highly accurate in spotting various cervical anomalies across several cases. This work advances the development of sophisticated computer-aided diagnostic systems, facilitating more rapid and accurate detection of cervical cancer, thereby significantly enhancing patient care outcomes.

QCML: Qualified Contrastive Machine Learning methodology for infectious disease diagnosis in CT images

The COVID-19 pandemic has had a terrible effect on human health, and computer-aided diagnostic (CAD) systems for chest computed tomography have emerged as a potential alternative for COVID-19 diagnosis. Yet, since the cost of data annotation may be excessively costly in the medical area, there is a shortage of data that has been annotated. A considerable quantity of labelled data is required in order to train a CAD system to a high level of accuracy. The study aims to describe an automatic and precise COVID-19 diagnostic method that utilizes a restricted amount of labelled CT images to solve this problem. The framework of the system is known as Qualified Contrastive Machine Learning (QCML), and the improvements that we have made may be summed up as follows: 1) In order to make use of all of the image's characteristics, we combine features with a two-dimensional discrete wavelet transform. 2) We employ the COVID-Net encoder with a redesign that focuses on the efficiency of learning and the task specificity of the data. 3) In order to strengthen our capacity to generalize, we have implemented a novel pertaining technique that is based on Qualified Contrastive Machine Learning. 4) In order to get better categorization results, we have included an extra auxiliary work. The application of Qualified Contrastive Machine Learning methodology for infectious disease diagnosis in CT images offers an accuracy of 93.55%, a recall of 91.59%, a precision of 96.92%, and an F1-score of 94.18%, demonstrating the potential for accurate and efficient COVID-19 diagnosis with limited labelled data.

A Machine Learning Approach for Detection and Classification of Colon Cancer using Convolutional Neural Network Architecture

In both developing and developed countries, cancer has become one of the most fearsome health ailments in the 21st century. Among the different variants of cancer, colon cancer is the 3rd most prevalent cancer globally and the 2nd deadliest form of cancer. Early diagnosis is very critical for the successful treatment of colon cancer. In recent times, machine-learning approaches have made significant strides in IoT-assisted healthcare systems. Computer-aided diagnostic systems driven by deep learning algorithms can detect colon cancer with great accuracy, assisting the medical profession in quick diagnosis and developing quick remedies against it. In our approach, we have developed a deep learning model based on convolutional neural network architecture to accurately classify and detect colon cancer. The developed model has been trained with 10,000 histopathological images of the colon, divided into two classes: colon adenocarcinoma and colon benign tissues, each containing 5000 images. We have also employed various performance metrics in our research to monitor the performance of our machine-learning model. The proposed model has been trained for 30 epochs with a batch size of 32 and achieved an overall accuracy of 98.79%.

Artificial intelligence for computer aided detection of pneumoconiosis: A succinct review since 1974

With the increasing availability of medical imaging data and advancements in artificial intelligence (AI) and computer vision (CV) techniques, computer aided diagnostic (CAD) systems have been consistently developed to help radiologists in the detection of pneumoconiosis. Pneumoconiosis is a respiratory disease caused by long term exposure of industrial dust. Pneumoconiosis has remained prevalent, even in countries with sophisticated healthcare systems and strict workplace measures. Consequently, this review article focuses on cascading the literature of existing CAD systems for pneumoconiosis diagnosis since 1974, providing a baseline for future research in this domain. For this purpose, 58 relevant articles were first selected after employing strict inclusion criteria, through 10 reliable databases and search engines, including Scopus, IEEE, Google Scholar, and PubMed etc. This review then systematically categorizes the selected CAD studies into two patterns, based on the employed methodology for pneumoconiosis diagnosis: texture-based and non-texture-based CAD systems. This study reveals that texture-based methods have been extensively adopted for pneumoconiosis diagnosis compared to non-texture-based methods (or deep learning based). However, deep learning approaches have shown superior performance thanks to the recent availability of large annotated CXR datasets and development of deep convolutional neural network (CNN) and transformer-based architectures. Finally, the analysis is concluded with a discussion on the shortcomings of current CAD systems and some suggested future directions for the development of effective diagnostic systems. Additionally, a number of benchmark datasets have also been discussed.

Automatic Maize Leaf Disease Recognition Using Deep Learning

Maize leaf diseases exhibit visible symptoms and are currently diagnosed by expert pathologists through personal observation, but the slow manual detection methods and pathologist's skill influence make it challenging to identify diseases in maize leaves. Therefore, computer-aided diagnostic systems offer a promising solution for disease detection issues. While traditional machine learning methods require perfect manual feature extraction for image classification, deep learning networks extract image features autonomously and function without pre-processing. This study proposes using the EfficientNet deep learning model for the classification of maize leaf diseases and compares it with another established deep learning model. The maize leaf disease dataset was used to train all models, with 4188 images for the original dataset and 6176 images for the augmented dataset. The EfficientNet B6 model achieved 98.10% accuracy on the original dataset, while the EfficientNet B3 model achieved the highest accuracy of 99.66% on the augmented dataset.

Comparative Performance Analysis of Time-Frequency Domain Images and Raw Signal Data for Classification of ECG Signals

ECG signals are one of the most common tools used to diagnose cardiovascular diseases. ECG signals are obtained by measuring electrical changes on the skin surface. Arrhythmias occurring in the heart are diagnosed because the expert evaluates ECG signals. This diagnosis depends on the experience of the specialist and is a subjective evaluation. With the widespread use of computer-aided diagnostic systems, evaluations dependent on the expert's experience are objectified, and support is provided to the physician for diagnosis. For computer-aided ECG classification, beats are detected from ECG signals, and arrhythmias are detected by analyzing the structure of these beats. In recent years, deep learning models have been successful in classifying ECG signals. The data to be used in the classification process is realized with the help of morphological features or images of the signal. The main objective of this study is to compare the classification performance of digital and visual heartbeat data for ECG signal classification. For this purpose, 1D-CNN and 2D-CNN architectures are used for the type of ECG signals. As inputs of the 1D-CNN model, numerical values of the heartbeat signal and hand-crafted features obtained from these numerical values were used. The inputs of the 2D-CNN model are the raw signal image, spectrogram, scalogram, Mel-spectrogram, GFCC, and CQT images, which are visual representations of the heartbeat signal. The results show that the numerical model of the ECG signal fails for classification, while the hand-crafted features provide 85.2% accuracy. The results obtained with the visual representation of the signal provided over 99% classification accuracy for all images. The highest success rate was 99.9% with the visualization of the raw signal. In line with these findings, the 2D-CNN architecture and the visual representation of the heartbeat signal were found to be the most suitable method for classifying ECG signals.

A dual data stream hybrid neural network for classifying pathological images of lung adenocarcinoma

Lung cancer has seriously threatened human health due to its high lethality and morbidity. Lung adenocarcinoma, in particular, is one of the most common subtypes of lung cancer. Pathological diagnosis is regarded as the gold standard for cancer diagnosis. However, the traditional manual screening of lung cancer pathology images is time consuming and error prone. Computer-aided diagnostic systems have emerged to solve this problem. Current research methods are unable to fully exploit the beneficial features inherent within patches, and they are characterized by high model complexity and significant computational effort. In this study, a deep learning framework called Multi-Scale Network (MSNet) is proposed for the automatic detection of lung adenocarcinoma pathology images. MSNet is designed to efficiently harness the valuable features within data patches, while simultaneously reducing model complexity, computational demands, and storage space requirements. The MSNet framework employs a dual data stream input method. In this input method, MSNet combines Swin Transformer and MLP-Mixer models to address global information between patches and the local information within each patch. Subsequently, MSNet uses the Multilayer Perceptron (MLP) module to fuse local and global features and perform classification to output the final detection results. In addition, a dataset of lung adenocarcinoma pathology images containing three categories is created for training and testing the MSNet framework. Experimental results show that the diagnostic accuracy of MSNet for lung adenocarcinoma pathology images is 96.55 %. In summary, MSNet has high classification performance and shows effectiveness and potential in the classification of lung adenocarcinoma pathology images.

Construction of deep learning-based convolutional neural network model for automatic detection of fluid hysteroscopic endometrial micropolyps in infertile women with chronic endometritis

Objective(s)Chronic endometritis (CE) is a localized mucosal inflammatory disorder associated with female infertility of unknown etiology, endometriosis, tubal factors, repeated implantation failure, and recurrent pregnancy loss, along with atypical uterine bleeding and iron deficiency anemia. Diagnosis of CE has traditionally relied on endometrial biopsy and detection of CD138(+) endometrial stromal plasmacytes. To develop a less invasive diagnostic system for CE, we aimed to construct a deep learning-based convolutional neural network (CNN) model for the automatic detection of endometrial micropolyps (EMiP), a fluid hysteroscopy (F-HSC) finding recognized as tiny protrusive lesions that are closely related to this disease. Study designThis is an in silico study using archival images of F-HSC performed at an infertility center in a private clinic. A total of 244 infertile women undergoing F-HSC on the days 6–12 of the menstrual cycle between April 2019 and December 2021 with histopathologically-confirmed CE with the aid of immunohistochemistry for CD138 were utilized. ResultsThe archival F-HSC images of 208 women (78 with EMiP and 130 without EMiP) who met the inclusion criteria were finally subjected to analysis. Following preprocessing of the images, half a set was input into a CNN architecture for training, whereas the remaining images were utilized as the test set to evaluate the performance of the model, which was compared with that of the experienced gynecologists. The sensitivity, specificity, accuracy, precision, and F1-score of the CNN model-aided diagnosis were 93.6 %, 92.3 %, 92.8 %, 88.0 %, and 0.907, respectively. The area under the receiver operating characteristic curves of the CNN model-aided diagnosis (0.930) was at a similar level (p > .05) to the value of conventional diagnosis by three experienced gynecologists (0.927, 0.948, and 0.906). ConclusionThese findings indicate that our deep learning-based CNN is capable of recognizing EMiP in F-HSC images and holds promise for further development of the computer-aided diagnostic system for CE.

Enhancing lung cancer diagnosis with data fusion and mobile edge computing using DenseNet and CNN

The recent advancements in automated lung cancer diagnosis through the application of Convolutional Neural Networks (CNN) on Computed Tomography (CT) scans have marked a significant leap in medical imaging and diagnostics. The precision of these CNN-based classifiers in detecting and analyzing lung cancer symptoms has opened new avenues in early detection and treatment planning. However, despite these technological strides, there are critical areas that require further exploration and development. In this landscape, computer-aided diagnostic systems and artificial intelligence, particularly deep learning methods like the region proposal network, the dual path network, and local binary patterns, have become pivotal. However, these methods face challenges such as limited interpretability, data variability handling issues, and insufficient generalization. Addressing these challenges is key to enhancing early detection and accurate diagnosis, fundamental for effective treatment planning and improving patient outcomes. This study introduces an advanced approach that combines a Convolutional Neural Network (CNN) with DenseNet, leveraging data fusion and mobile edge computing for lung cancer identification and classification. The integration of data fusion techniques enables the system to amalgamate information from multiple sources, enhancing the robustness and accuracy of the model. Mobile edge computing facilitates faster processing and analysis of CT scan images by bringing computational resources closer to the data source, crucial for real-time applications. The images undergo preprocessing, including resizing and rescaling, to optimize feature extraction. The DenseNet-CNN model, strengthened by data fusion and edge computing capabilities, excels in extracting and learning features from these CT scans, effectively distinguishing between healthy and cancerous lung tissues. The classification categories include Normal, Benign, and Malignant, with the latter further sub-categorized into adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. In controlled experiments, this approach outperformed existing state-of-the-art methods, achieving an impressive accuracy of 99%. This indicates its potential as a powerful tool in the early detection and classification of lung cancer, a significant advancement in medical imaging and diagnostic technology.

Computer-aided diagnostic system with automated deep learning method based on the AutoGluon framework improved the diagnostic accuracy of early esophageal cancer.

There have been studies on the application of computer-aided diagnosis (CAD) in the endoscopic diagnosis of early esophageal cancer (EEC), but there is still a significant gap from clinical application. We developed an endoscopic CAD system for EEC based on the AutoGluon framework, aiming to explore the feasibility of automatic deep learning (DL) in clinical application. The endoscopic pictures of normal esophagus, esophagitis, and EEC were collected from The First Affiliated Hospital of Soochow University (September 2015 to December 2021) and the Norwegian HyperKvasir database. All images of non-cancerous esophageal lesions and EEC in this study were pathologically examined. There were three tasks: task A was normal vs. lesion classification under non-magnifying endoscopy (n=932 vs. 1,092); task B was non-cancer lesion vs. EEC classification under non-magnifying endoscopy (n=594 vs. 429); and task C was non-cancer lesion vs. EEC classification under magnifying endoscopy (n=505 vs. 824). In all classification tasks, we took 100 pictures as the verification set, and the rest comprised as the training set. The CAD system was established based on the AutoGluon framework. Diagnostic performance of the model was compared with that of endoscopists grouped according to years of experience (senior >15 years; junior <5 years). Model evaluation indicators included accuracy, recall rate, precision, F1 value, interpretation time, and the area under the receiver operating characteristic (ROC) curve (AUC). In tasks A and B, the accuracies of medium-performance CAD and high-performance CAD were lower than those of junior doctors and senior doctors. In task C, the medium-performance and high-performance CAD accuracies were close to those of junior doctors and senior doctors. The high-performance CAD model outperformed the junior doctors in both task A (0.850 vs. 0.830) and task C (0.840 vs. 0.830) in sensitivity comparison, but there was still a large gap between high-performance CAD models and doctors in sensitivity comparison. In task A, with the aid of CAD pre-interpretation, the accuracy of junior and senior physicians were significantly improved (from 0.880 to 0.915 and from 0.920 to 0.945, respectively); the time spent on film reading was significantly shortened (junior: from 11.3 to 8.7 s; senior: from 6.7 to 5.5 s). In task C, with the aid of CAD pre-interpretation, the accuracy of junior and senior physicians were significantly improved (from 0.850 to 0.865 and from 0.915 to 0.935, respectively); the reading time was significantly shortened (junior: from 9.5 to 7.7 s; senior: from 5.6 to 3.0 s). The CAD system based on the AutoGluon framework can assist doctors to improve the diagnostic accuracy and reading time of EEC under endoscopy. This study reveals that automatic DL methods are promising in clinical application.

Detection of multi‐class lung diseases based on customized neural network

AbstractIn the medical image processing domain, deep learning methodologies have outstanding performance for disease classification using digital images such as X‐rays, magnetic resonance imaging (MRI), and computerized tomography (CT). However, accurate diagnosis of disease by medical personnel can be challenging in certain cases, such as the complexity of interpretation and non‐availability of expert personnel, difficulty at pixel‐level analysis, etc. Computer‐aided diagnostic (CAD) systems with proper training have shown the potential to enhance diagnostic accuracy and efficiency. With the exponential growth of medical data, CAD systems can analyze and extract valuable information by assisting medical personnel during the disease diagnostic process. To overcome these challenges, this research introduces CX‐RaysNet, a novel deep‐learning framework designed for the automatic identification of various lung disease classes in digital chest X‐ray images. The core novelty of the CX‐RaysNet framework lies in the integration of both convolutional and group convolutional layers, along with the usage of small filter sizes and the incorporation of dropout regularization. This phenomenon helps us optimize the model's ability to distinguish minute features that reveal different lung diseases. Additionally, data augmentation techniques are implemented to augment the training and testing datasets, which enhances the model's robustness and generalizability. The performance evaluation of CX‐RaysNet reveals promising results, with the proposed model achieving a multi‐class classification accuracy of 97.25%. Particularly, this study represents the first attempt to optimize a model specifically for low‐power embedded devices, aiming to improve the accuracy of disease detection while minimizing computational resources.

Preliminary Study Based on Myocardial Infarction Classification of 12-Lead Electrocardiography Images with Deep Learning Methods

In contemporary medicine, the development of computer-aided diagnostic systems using Electrocardiography (ECG) signals has gained significance for the diagnosis of heart diseases. Myocardial infarction (MI) is recognized as the condition where blood flow to the heart muscle is obstructed due to blockages in coronary vessels. In this study, four deep learning approaches were employed to automatically identify different MI conditions (STEMI, NSTEMI, USAP) using images generated from 12-lead ECG signals. The utilized architectures include deep neural networks such as Visual Geometry Group-16 (VGG-16), AlexNet, Residual Neural Network (ResNet), SqueezeNet and an ensemble model composed of these networks. With the proposed method, classification was performed based on 10-second grayscale images of 12-lead ECG signals for HC-STEMI, HC-NSTEMI, HC-USAP, and NSTEMI-STEMI conditions. According to the obtained results, the HC-STEMI group achieved the highest performance with a cross-validated 0.8237 F1 score using the AlexNet architecture. Among the novel contributions of this study is the image-based ECG classification method that can be more easily adapted to clinical applications and the analysis of the potential use of detecting different MI conditions in clinical practices. In conclusion, this study sheds light on future research by demonstrating the significant potential of using multi-channel ECG signals in image format for MI diagnosis, paving the way for advancements in this field.

Ensemble Deep Learning Methods for Detecting Skin Cancer

Skin cancer is a common and possibly fatal condition. Effective treatment results are greatly influenced by early identification. Deep learning (DP) algorithms have demonstrated encouraging outcomes in skin cancer detection computer-aided diagnostic systems. This article investigates the many forms of skin cancer, such as melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC), and offers a system for detecting skin cancer utilizing convolutional neural network (CNN) approaches, particularly the multi-model ResNet (M-ResNet) architecture. We present a ResNet architecture that is capable of handling deep networks and has increased skin cancer detection performance. The proposed approach uses a thorough pipeline to find skin cancer. The dataset first goes through pre-processing (PP) procedures, such as picture resizing, normalization, and augmentation approaches, to improve the model's capacity for generalization. The multi-model assembles, leading to improved accuracy, sensitivity, and specificity in skin cancer LEARNING Classification SYSTEM (SC-LCS) tasks. In this study FINAL highlights, the effectiveness of deep learning (DL)techniques, specifically the multi-model ResNet architecture, AND skin cancer LEARNING classification SYSTEM (SC-LCS) for skin cancer detection. The suggested framework seems to have promising results in accurately identifying different types of skin cancer, assisting in diagnosis and therapy at an early stage. Further research and development in this field can potentially contribute to improving healthcare systems and reducing the global burden of skin cancer-related EFFECTED and DEATH RATE.

Computer-Aided Diagnosis of Maxillary Sinus Anomalies: Validation and Clinical Correlation.

Computer aided diagnostics (CAD) systems can automate the differentiation of maxillary sinus (MS) with and without opacification, simplifying the typically laborious process and aiding in clinical insight discovery within large cohorts. This study uses Hamburg City Health Study (HCHS) a large, prospective, long-term, population-based cohort study of participants between 45 and 74 years of age. We develop a CAD system using an ensemble of 3D Convolutional Neural Network (CNN) to analyze cranial MRIs, distinguishing MS with opacifications (polyps, cysts, mucosal thickening) from MS without opacifications. The system is used to find correlations of participants with and without MS opacifications with clinical data (smoking, alcohol, BMI, asthma, bronchitis, sex, age, leukocyte count, C-reactive protein, allergies). The evaluation metrics of CAD system (Area Under Receiver Operator Characteristic: 0.95, sensitivity: 0.85, specificity: 0.90) demonstrated the effectiveness of our approach. MS with opacification group exhibited higher alcohol consumption, higher BMI, higher incidence of intrinsic asthma and extrinsic asthma. Male sex had higher prevalence of MS opacifications. Participants with MS opacifications had higher incidence of hay fever and house dust allergy but lower incidence of bee/wasp venom allergy. The study demonstrates a 3D CNN's ability to distinguish MS with and without opacifications, improving automated diagnosis and aiding in correlating clinical data in population studies. 3 Laryngoscope, 2024.

Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform

Colorectal cancer (CRC) exhibits a significant death rate that consistently impacts human lives worldwide. Histopathological examination is the standard method for CRC diagnosis. However, it is complicated, time-consuming, and subjective. Computer-aided diagnostic (CAD) systems using digital pathology can help pathologists diagnose CRC faster and more accurately than manual histopathology examinations. Deep learning algorithms especially convolutional neural networks (CNNs) are advocated for diagnosis of CRC. Nevertheless, most previous CAD systems obtained features from one CNN, these features are of huge dimension. Also, they relied on spatial information only to achieve classification. In this paper, a CAD system is proposed called “Color-CADx” for CRC recognition. Different CNNs namely ResNet50, DenseNet201, and AlexNet are used for end-to-end classification at different training–testing ratios. Moreover, features are extracted from these CNNs and reduced using discrete cosine transform (DCT). DCT is also utilized to acquire spectral representation. Afterward, it is used to further select a reduced set of deep features. Furthermore, DCT coefficients obtained in the previous step are concatenated and the analysis of variance (ANOVA) feature selection approach is applied to choose significant features. Finally, machine learning classifiers are employed for CRC classification. Two publicly available datasets were investigated which are the NCT-CRC-HE-100 K dataset and the Kather_texture_2016_image_tiles dataset. The highest achieved accuracy reached 99.3% for the NCT-CRC-HE-100 K dataset and 96.8% for the Kather_texture_2016_image_tiles dataset. DCT and ANOVA have successfully lowered feature dimensionality thus reducing complexity. Color-CADx has demonstrated efficacy in terms of accuracy, as its performance surpasses that of the most recent advancements.

Harnessing the power of radiomics and deep learning for improved breast cancer diagnosis with multiparametric breast mammography

Breast cancer, with its high mortality, faces diagnostic challenges due to variability in mammography quality and breast densities, leading to inconsistencies in radiological interpretations. Computer-aided diagnostic (CAD) systems, while helpful, struggle with accurately interpreting lesion characteristics such as morphology, density, and size. To address this, our study developed advanced deep-learning algorithms to improve the detection, localization, risk assessment, and classification of breast lesions, aiming to reduce false positives on human intervention and tackle slow convergence rates. Key innovations of the approach include preprocessing techniques, advanced filtering, and data augmentation strategies to optimize model performance, mitigating over- and under-fitting concerns. A significant development is the Chaotic Leader Selective Filler Swarm Optimization (cLSFSO) method, which effectively detects breast-dense lesions by extracting textural and statistical features. Additionally, the study adapted deep learning models like modified VGGNet and SE-ResNet152 through transfer learning, significantly enhancing their capability to distinguish between normal and suspicious mammography regions. The study also introduces hybrid deep neural network-based approaches, including CNN+LSTM and CNN+SVM, for diagnosing and grading cancerous polyps from the pre-segmented ROIs. Besides, the transfer learning paradigm is employed to boost the efficacy in classifying breast masses and reducing computing time by modifying the final layer of the proposed pre-trained models. The integration of Grad-CAM techniques further refines our analysis, leading to more accurate assessments and improved diagnosis of breast anomalies. Evaluated using benchmark and private datasets, our algorithms demonstrated a sensitivity of 0.99 and an overall AUC of 0.99, indicating significant improvements in mammogram analysis. These advancements aid radiologists, potentially improving patient outcomes and contributing to medical imaging and AI in healthcare.

Current Status of Machine Learning and Artificial Intelligence in Cervical Cancer Screening and Diagnosis: A Systematic Review

Background: Cervical cancer poses a substantial global health challenge, predominantly affecting underprivileged countries. The limitations of current screening methods, such as cytology and visual examination, underscore the need for improved techniques. Artificial intelligence (AI) and machine learning (ML), particularly convolutional neural networks, offer promising solutions in this regard. Methodology: Fifteen studies meeting the inclusion criteria were examined. The PRISMA criteria guided the exploration of cervical cancer screening studies employing AI, ML, and deep learning on PubMed/MEDLINE and Google Scholar. The search focused on "artificial intelligence" and "Pap smear." The investigation specifically delves into English-language studies post-2019 that pertain to the machine learning and deep learning classification of cervical cancer using mobile devices. Histology, animal research, and pre-2019 investigations are excluded. Titles and abstracts were carefully reviewed for any discrepancies and subsequently discussed. The process of data extraction involved compiling information from the selected articles. Result: The systematic review investigates the impact of AI and ML on cervical cancer detection, screening, and diagnosis. Our review reveals enhanced accuracy and efficiency in innovative technologies such as CytoBrain and computer-aided diagnostic systems employing Compact VGG and ResNet101. ML techniques like logistic regression, MLP, SVM, KNN, and naive Bayes prove beneficial for managing complex datasets, particularly when combined with class-balancing procedures. The promising aspects include the application of deep learning for automation and AI-assisted digital microscopy. These findings signify a transformative shift in cervical cancer screening, underscoring the potential of ML and AI technologies to elevate diagnostic accuracy and accessibility. Conclusion: Our study demonstrates advancements in both accuracy and responsiveness. Despite recognizing scientific and ethical considerations, the study underscores the potential of AI to enhance cervical cancer care. This systematic review advocates for policymakers and healthcare practitioners to use ongoing research for informed decision-making in this rapidly evolving field.