Computer-aided Diagnosis System Research Articles

DenseIncepS115: a novel network-level fusion framework for Alzheimer's disease prediction using MRI images

One of the most prevalent disorders relating to neurodegenerative conditions and dementia is Alzheimer's disease (AD). In the age group 65 and older, the prevalence of Alzheimer's disease is increasing. Before symptoms showed up, the disease had grown to a severe stage and resulted in an irreversible brain disorder that is not treatable with medication or other therapies. Therefore, early prediction is essential to slow down AD progression. Computer-aided diagnosis systems can be used as a second opinion by radiologists in their clinics to predict AD using MRI scans. In this work, we proposed a novel deep learning architecture named DenseIncepS115for for AD prediction from MRI scans. The proposed architecture is based on the Inception Module with Self-Attention (InceptionSA) and the Dense Module with Self-Attention (DenseSA). Both modules are fused at the network level using a depth concatenation layer. The proposed architecture hyperparameters are initialized using Bayesian Optimization, which impacts the better learning of the selected datasets. In the testing phase, features are extracted from the depth concatenation layer, which is further optimized using the Catch Fish Optimization (CFO) algorithm and passed to shallow wide neural network classifiers for the final prediction. In addition, the proposed DenseIncepS115 architecture is interpreted through Lime and Gradcam explainable techniques. Two publicly available datasets were employed in the experimental process: Alzheimer's ADNI and Alzheimer's classes MRI. On both datasets, the proposed architecture obtained an accuracy level of 99.5% and 98.5%, respectively. Detailed ablation studies and comparisons with state-of-the-art techniques show that the proposed architecture outperforms.

HFF-Net: A hybrid convolutional neural network for diabetic retinopathy screening and grading

Diabetic Retinopathy (DR) is a leading cause of vision loss among diabetic patients, necessitating effective screening and grading for timely intervention. Regular screening significantly increases the workload of ophthalmologists, and accurate grading into stages—mild, moderate, severe, and proliferative—is crucial for monitoring disease progression. While Computer-Aided Diagnosis (CAD) systems can alleviate this burden, existing Convolutional Neural Network (CNN)-based frameworks use fixed-size kernels in a linear feed-forward manner. This approach can lead to information loss in the initial stages due to limited feature utilization across adjacent layers. To address this limitation, we propose a Hierarchical Features Fusion Convolutional Neural Network (HFF-Net) within a Diabetic Retinopathy Screening and Grading (DRSG) framework. The framework includes preprocessing to extract regions of interest from fundus images (FIs), enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE), and data augmentation for class balancing and overfitting mitigation. HFF-Net extracts multiscale features that fused at multiple levels within the network, utilizing the swish activation function for improved learning stability. We evaluated HFF-Net against several state-of-the-art CNN classifiers within the DRSG framework. Experimental results demonstrate that HFF-Net achieves a grading accuracy of 73.77 ​%, surpassing the second-best model by 3.51 percentage points (a relative improvement of approximately 5 ​%), and attains a screening accuracy of 98.70 ​% using only 1.18 million parameters. These findings highlight HFF-Net's potential as an efficient and effective tool in CAD systems for DR screening and grading.©2017 Elsevier Inc. All rights reserved.

Graph regularized least squares regression for automated breast ultrasound imaging

Breast cancer, recognized as one of the most pervasive malignancies affecting females, manifests a perpetual escalation in its worldwide morbidity. Timely screening offers patients interventions that are not only more expeditious but also more efficacious. ABUS, as an automated breast ultrasound imaging technology, has the advantages of high resolution, reproducible results, and strong objectivity. However, manual interpretation of ABUS images can be highly tedious and laborious when handling huge amounts of data from images, may leading to the risk of missed diagnosis. In practical clinical diagnosis, the incorporation of Computer-Aided Diagnosis (CAD) systems emerges as a more favorable option. In consideration of the holistic factors encompassing model construction efficiency, training cost and overall performance, we provide a unique subspace clustering approach known as Graph regularized Least Squares Regression (GLSR) in this paper. Firstly, we encode the data's intrinsic geometry into the framework of least squares regression to respect local and global correlation structures at the same time. Secondly, we construct a nearest neighbor graph to model the smooth structure of the representation, which may lead to a more accurate representation. Finally, a nonnegative constraint is introduced into our framework so that the representation obtained by GLSR has clearer physical meaning. We provide an optimization approach for solving such a framework based on iterative updates of four blocks. Extensive experimental results indicate the effectiveness of the proposed method compared with state-of-the-art approaches on real-world breast cancer images and other biological data.

Exploring transformer reliability in clinically significant prostate cancer segmentation: A comprehensive in-depth investigation.

Despite the growing prominence of transformers in medical image segmentation, their application to clinically significant prostate cancer (csPCa) has been overlooked. Minimal attention has been paid to domain shift analysis and uncertainty assessment, critical for safely implementing computer-aided diagnosis (CAD) systems. Domain shift in medical imagery refers to differences between the data used to train a model and the data evaluated later, arising from variations in imaging equipment, protocols, patient populations, and acquisition noise. While recent models enhance in-domain performance, areas such as robustness and uncertainty estimation in out-of-domain distributions have received limited investigation, creating indecisiveness about model reliability. In contrast, our study addresses csPCa at voxel, lesion, and image levels, investigating models from traditional U-Net to cutting-edge transformers. We focus on four key points: robustness, calibration, out-of-distribution (OOD), and misclassification detection (MD). Findings show that transformer-based models exhibit enhanced robustness at image and lesion levels, both in and out of domain. However, this improvement is not fully translated to the voxel level, where Convolutional Neural Networks (CNNs) outperform in most robustness metrics. Regarding uncertainty, hybrid transformers and transformer encoders performed better, but this trend depends on misclassification or out-of-distribution tasks.

ProtoMed: Prototypical networks with auxiliary regularization for few-shot medical image classification

Although deep learning has shown impressive results in computer vision, the scarcity of annotated medical images poses a significant challenge for its effective integration into Computer-Aided Diagnosis (CAD) systems. Few-Shot Learning (FSL) opens promising perspectives for image recognition in low-data scenarios. However, applying FSL for medical image diagnosis presents significant challenges, particularly in learning disease-specific and clinically relevant features from a limited number of images. In the medical domain, training samples from different classes often exhibit visual similarities. Consequently, certain medical conditions may present striking resemblances, resulting in minimal inter-class variation. In this paper, we propose a prototypical network-based approach for few-shot medical image classification for low-prevalence diseases detection. Our method leverages meta-learning to use prior knowledge gained from common diseases, enabling generalization to new cases with limited data. However, the episodic training inherent in meta-learning tends to disproportionately emphasize the connections between elements in the support set and those in the query set, which can compromise the understanding of complex relationships within medical image data during the training phase. To address this, we propose an auxiliary network as a regularizer in the meta-training phase, designed to enhance the similarity of image representations from the same class while enforcing dissimilarity between representations from different classes in both the query and support sets. The proposed method has been evaluated using three medical diagnosis problems with different imaging modalities and different levels of visual imaging details and patterns. The obtained model is lightweight and efficient, demonstrating superior performance in both efficiency and accuracy compared to state-of-the-art. These findings highlight the potential of our approach to improve performance in practical applications, balancing resource limitations with the need for high diagnostic accuracy.

Comparative effectiveness of chest ultrasound, chest X-ray and computer-aided diagnostic (CAD) for tuberculosis diagnosis in low-resource setting: study protocol for a cross-sectional study from Ethiopia

IntroductionEarly and accurate diagnosis of pulmonary tuberculosis (TB) is crucial for timely treatment and prevention of transmission, but diagnostic challenges persist due to complex symptoms and limitations in diagnostic tools. Chest X-ray (CXR) is the standard imaging modality, but its sensitivity and specificity may vary. Recently, some promising alternatives emerged such as chest ultrasonography (CUS) – particularly valuable in resource-limited settings – and computer-aided diagnosis (CAD) systems – helping clinicians in the reading and interpretation of the CXR. However, direct comparisons of CUS, CXR, and CAD score in TB diagnosis are limited.Methods and analysisThis cross-sectional study will assess the diagnostic effectiveness of CUS in diagnosing TB compared to CXR and CAD score among index cases and household contacts. The study will be conducted at Wolisso St. Luke Hospital (Wolisso, Ethiopia). Index cases will be subjects with diagnosis of pulmonary tuberculosis within 7 days. Household contacts will be identified by administering a screening questionnaire to index cases. They will undergo CXR as for standard of care and consequent CAD analysis and CUS. The anticipated sample size is 136 subjects. The common accuracy metrics (sensitivity, specificity, positive and negative predictive values) will be calculated.Ethics and disseminationThe protocol was approved by the Oromia Health Bureau Research Ethics Committee (BFO/MBTFH/1-16/1908). All information obtained will be confidential. Selected investigators will have access to data, while international partners will sign a dedicated Data Protection Agreement. Eligible subjects will receive a brief information about the study before being asked to participate and they will provide a written informed consent. Results will be conveyed to stakeholders and disseminated through conferences and peer-reviewed journals.Clinical trial registrationNCT06409780, https://clinicaltrials.gov.

Development of an automated tool for the estimation of histological remission in ulcerative colitis using single wavelength endoscopy technology.

Ulcerative colitis (UC) management employs a strategy targeting histological and endoscopic remission. Correlation of white-light endoscopy (WLE) scores with histological activity is limited. Single-wavelength endoscopy (SWE) addressing microvascular changes reflecting histological disease activity, may better assess histological remission. Our goal was to assess the accuracy of a computer-aided diagnosis (CAD) system for histological activity estimation in UC, based on either WLE or SWE. We collected 6926 sets of corresponding WLE and SWE frames in 112 patients with UC, using a prototype endoscopic system enabling both imaging methods (FUJIFILM, Tokyo, Japan). Histological remission (Geboes score ≤2B.0) assessed at the location of imaging was annotated for all frames and separate WLE-CAD and SWE-CAD models were trained using deep learning for automated detection of histological remission with either imaging modality. Initial training of both models on the same subset of 42 patients, resulted in SWE-CAD outperforming WLE-CAD with a mean sensitivity of 88.0% vs 73.9% (p < 0.001), a mean specificity of 71.7% vs 65.6% (p=0.45), and a diagnostic accuracy of 83.3% vs 67.5% (p<0.005), respectively. Further training of the SWE-CAD model on the entire dataset of 112 patients resulted in SWE-CAD achieving a 95.2% accuracy, 96.4% sensitivity, and 92.9% specificity on a section level. By utilizing automated CAD based on non-magnifying SWE for enhanced capillary visibility versus WLE, histological remission was detected with 95.2% diagnostic accuracy in patients with UC, offering stable objectivity and helping to exclude inter-reader variability.

Unveiling human eye temperature with deep learning-powered segmentation

Thermal imaging technology presents a promising, non-invasive tool for medical diagnostics, particularly in eye health assessment. However, accurately delineating the eye region in thermal images of the human face remains a significant challenge for temperature analysis. Extracting eye temperature information from ocular thermal images has traditionally been a manual or semi-automated process, making it time-consuming and tedious. Our research aims to address these issues by introducing an automated systematic approach to eye segmentation and temperature analysis. We employ the semantic segmentation deep learning model SegFormer to segment the eye region and a novel method to extract the thermal-ocular temperature within this region of interest (ROI) for analysis of temperature patterns and diagnosis of any abnormalities. The process starts by extracting temperature data from each pixel in the thermal images. Using the mask and these temperature values, only the temperatures within the masked area are isolated for analysis. Validation is done by comparing and calculating the mean square error against the manual method. A self-created dataset of thermal images of human faces is used due to the lack of available datasets for segmentation. SegFormer (B5) achieves a mean intersection over union (MIoU) of 0.9178 and a dice coefficient of 0.9299. The extracted temperature values from the segmented eye regions provide crucial data for assessing eye health and identifying potential abnormalities. This methodical approach contributes to the progression of thermal image analysis for diagnosing eye-related conditions, particularly in ophthalmology, and in medical domains such as fever screening and reliable Computer-Aided Diagnosis Systems (CADS).

MBF-Net: Multi-scale boundary-aware aggregation for bi-directional information exchange and feature reshaping for medical image segmentation

As a critical component of computer-aided diagnosis systems, medical image segmentation plays a vital role in assisting clinicians in making rapid and accurate decisions and formulating treatment plans. Nevertheless, precise medical image segmentation still presents a number of challenges, including insufficient feature extraction capabilities in the presence of limited sample sizes, blurred segmentation boundaries, and information loss between the encoder and decoder. In order to address these issues, we propose a Multi-Scale Boundary-Aware Aggregation Network with Bidirectional Information Exchange and Feature Refinement (MBF-Net) for medical image segmentation. Initially, we design a Multi-Scale Boundary-Aware Aggregation Encoder (MBAE) that aggregates features from different scales and pixel levels within the input images, capturing fine-grained boundary information in deep features and establishing comprehensive global and local multi-scale contextual dependencies. This design significantly enhances the model's understanding of the overall image structure and its ability to discern subtle differences between lesions and background. Subsequently, a Multi-Scale Bidirectional Information Transmission (MBIT) module is introduced, which integrates bidirectional information flow between low-level and high-level features, enabling multi-scale features to flow bidirectionally across different layers. The MBIT module effectively preserves crucial boundary details during cross-layer information transmission, thereby bridging the semantic gap between the encoder and decoder, and thereby improving the clarity of the segmentation boundaries. Finally, we develop a Feature Refinement and Aggregation Fusion (FRAF) module, designed to integrate feature information from various semantic levels, which alleviates discrepancies between features at varying scales, thus enhancing the segmentation accuracy of the network. The generalisation and effectiveness of MBF-Net are validated through comprehensive experiments on a range of tasks, including nuclear segmentation, breast cancer segmentation, polyp segmentation and skin lesion segmentation. Both subjective and objective evaluations demonstrate that MBF-Net significantly outperforms current state-of-the-art methods, achieving average Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) scores of 86.34 % and 78.37 %, respectively. The superior performance of MBF-Net in terms of segmentation accuracy and quality is demonstrated across five public datasets.

Medical Image Segmentation

Medical image segmentation is a critical component in the development of computer-aided diagnosis and treatment planning systems. This paper provides a comprehensive survey of recent advances in segmentation techniques applied to various imaging modalities, including Magnetic Resonance Imaging (MRI). Traditional methods such as thresholding, region-growing, and active contours are reviewed alongside contemporary machine learning-based approaches, particularly deep learning models. The survey emphasizes the growing dominance of convolutional neural networks (CNNs) and their variants, including U-Net and Fully Convolutional Networks (FCNs), which have shown remarkable success in handling complex medical imaging challenges. Additionally, the paper discusses hybrid methods that combine classical techniques with artificial intelligence to improve accuracy and robustness in segmentation tasks. Key challenges such as class imbalance, boundary delineation, and computational efficiency are also highlighted. Future directions, including the integration of multi-modal data and advancements in self-supervised learning, are explored as potential solutions to overcome current limitations in medical image segmentation.

Interpretable rough neural network for lung nodule diagnosis

Computer-aided diagnosis (CAD) systems based on deep learning have shown significant potential in lung nodule diagnosis, providing substantial assistance to medical professionals. However, the inherent lack of interpretability in deep learning models and the uncertainty of annotations limit their widespread application. We propose that uncertain annotations actually imply additional valuable information that can enhance both model performance and interpretability. To address these challenges, we have developed a novel soft computing methodology integrating rough sets with deep neural networks. Firstly, this methodology employs rough sets to process uncertain region of interest (ROI) annotations into upper and lower approximations. Secondly, a novel rough neuron is designed to predict these approximations. Thirdly, the newly proposed region-constraint strategy embeds interpretable radiological domain knowledge into the neural network. Finally, this methodology proposes interpretation curves and regional consistency metrics to quantitatively evaluate the model’s interpretability. We conducted extensive comparison experiments on LIDC-IDRI and LNDb public benchmarks. Detailed experimental results demonstrate that by maximally retaining uncertain samples, the proposed method achieves classification accuracies of 84.6% and 89.74%, and mean absolute errors of 0.4988 and 0.5208 in attribute prediction, representing improvements of 3.4% and 2.5%, respectively, over the backbone networks.

Differences in regions of interest to identify deeply invasive colorectal cancers: Computer-aided diagnosis vs expert endoscopists.

Background and study aims Diagnostic performance of a computer-aided diagnosis (CAD) system for deep submucosally invasive (T1b) colorectal cancer was excellent, but the "regions of interest" (ROI) within images are not obvious. Class activation mapping (CAM) enables identification of the ROI that CAD utilizes for diagnosis. The purpose of this study was a quantitative investigation of the difference between CAD and endoscopists. Patients and methods Endoscopic images collected for validation of a previous study were used, including histologically proven T1b colorectal cancers (n = 82; morphology: flat 36, polypoid 46; median maximum diameter 20 mm, interquartile range 15-25 mm; histological subtype: papillary 5, well 51, moderate 24, poor 2; location: proximal colon 26, distal colon 27, rectum 29). Application of CAM was limited to one white light endoscopic image (per lesion) to demonstrate findings of T1b cancers. The CAM images were generated from the weights of the previously fine-tuned ResNet50. Two expert endoscopists depicted the ROI in identical images. Concordance of the ROI was rated by intersection over union (IoU) analysis. Results Pixel counts of ROIs were significantly lower using 165K[x103] [108K-227K] than by endoscopists (300K [208K-440K]; P < 0.0001) and median [interquartile] of the IoU was 0.198 [0.024-0.349]. IoU was significantly higher in correctly identified lesions (n = 54, 0.213 [0.116-0.364]) than incorrect ones (n=28, 0.070 [0.000-0.2750, P = 0.033). Concusions IoU was larger in correctly diagnosed T1b colorectal cancers. Optimal annotation of the ROI may be the key to improving diagnostic sensitivity of CAD for T1b colorectal cancers.

Clustering classifier of FRP strengthened concrete beams using superpixels and principal component analysis

Nowadays, the application of Fiber Reinforced Polymer (FRP) materials in concrete structures is more and more extended because of their advantages. However, premature and brittle failure caused by debonding of FRP materials from the concrete surface may be critical and needs to be detected at its earliest stages to avoid possible catastrophic events. Digital image correlation (DIC) has been used in the past for the detection of abnormalities from surface digital images. In this work, an efficient computer-aided diagnosis (CAD) system for damage identification for this type of FRP strengthening is developed. It is based on the combination of cells of strain maps built with a DIC system and an automated clustering classifier optimized with the use of superpixels and principal component analysis. Additionally, preprocessing of the images is also done with some filters to enhance the accuracy of the methodology. The procedure has been validated with experimental results on a progressively damaged FRP externally strengthened beam. Results show that the proposed detection scheme achieves an effective automated classification method for the damage detection of this kind of strengthened specimens. Furthermore, the optimal choice of the subset size, number of superpixels and principal components contributed to the success of the method. Simultaneously, an attempt to quantify damage from the proposed method and its validation with electromechanical impedance method has been developed.

Deep Learning and Handcrafted Features for Thyroid Nodule Classification

ABSTRACTIn this research, we present a refined image‐based computer‐aided diagnosis (CAD) system for thyroid cancer detection using ultrasound imagery. This system integrates a specialized convolutional neural network (CNN) architecture designed to address the unique aspects of thyroid image datasets. Additionally, it incorporates a novel statistical model that utilizes a two‐dimensional random coefficient autoregressive (2D‐RCA) method to precisely analyze the textural characteristics of thyroid images, thereby capturing essential texture‐related information. The classification framework relies on a composite feature vector that combines deep learning features from the CNN and handcrafted features from the 2D‐RCA model, processed through a support vector machine (SVM) algorithm. Our evaluation methodology is structured in three phases: initial assessment of the 2D‐RCA features, analysis of the CNN‐derived features, and a final evaluation of their combined effect on classification performance. Comparative analyses with well‐known networks such as VGG16, VGG19, ResNet50, and AlexNet highlight the superior performance of our approach. The outcomes indicate a significant enhancement in diagnostic accuracy, achieving a classification accuracy of 97.2%, a sensitivity of 84.42%, and a specificity of 95.23%. These results not only demonstrate a notable advancement in the classification of thyroid nodules but also establish a new standard in the efficiency of CAD systems.

Revolutionizing Radiology With Artificial Intelligence.

Artificial intelligence (AI) is rapidly transforming the field of radiology, offering significant advancements in diagnostic accuracy, workflow efficiency, and patient care. This article explores AI's impact on various subfields of radiology, emphasizing its potential to improve clinical practices and enhance patient outcomes. AI-driven technologies such as machine learning, deep learning, and natural language processing (NLP) are playing a pivotal role in automating routine tasks, aiding in early disease detection, and supporting clinical decision-making, allowing radiologists to focus on more complex diagnostic challenges. Key applications of AI in radiology include improving image analysis through computer-aided diagnosis (CAD) systems, which enhance the detection of abnormalities in imaging, such as tumors. AI tools have demonstrated high accuracy in analyzing medical images, integrating data from multiple imaging modalities such as CT, MRI, and PET to provide comprehensive diagnostic insights. These advancements facilitate personalized treatment planning and complement radiologists' workflows. However, for AI to be fully integrated into radiology workflows, several challenges must be addressed, including ensuring transparency in how AI algorithms work, protecting patient data, and avoiding biases that could affect diverse populations. Developing explainable AI systems that can clearly show how decisions are made is crucial, as is ensuring AI tools can seamlessly fit into existing radiology systems. Collaboration between radiologists, AI developers, and policymakers, alongside strong ethical guidelines and regulatory oversight, will be key to ensuring AI is implemented safely and effectively in clinical practice. Overall, AI holds tremendous promise in revolutionizing radiology. Through its ability to automate complex tasks, enhance diagnostic capabilities, and streamline workflows, AI has the potential to significantly improve the quality and efficiency of radiology practices. Continued research, development, and collaboration will be crucial in unlocking AI's full potential and addressing the challenges that accompany its adoption.

Part-Prototype Models in Medical Imaging: Applications and Current Challenges

Recent developments in Artificial Intelligence have increasingly focused on explainability research. The potential of Explainable Artificial Intelligence (XAI) in producing trustworthy computer-aided diagnosis systems and its usage for knowledge discovery are gaining interest in the medical imaging (MI) community to support the diagnostic process and the discovery of image biomarkers. Most of the existing XAI applications in MI are focused on interpreting the predictions made using deep neural networks, typically including attribution techniques with saliency map approaches and other feature visualization methods. However, these are often criticized for providing incorrect and incomplete representations of the black-box models’ behaviour. This highlights the importance of proposing models intentionally designed to be self-explanatory. In particular, part-prototype (PP) models are interpretable-by-design computer vision (CV) models that base their decision process on learning and identifying representative prototypical parts from input images, and they are gaining increasing interest and results in MI applications. However, the medical field has unique characteristics that could benefit from more advanced implementations of these types of architectures. This narrative review summarizes existing PP networks, their application in MI analysis, and current challenges.

Deep Learning With Optical Coherence Tomography for Melanoma Identification and Risk Prediction.

Malignant melanoma is the most severe skin cancer with a rising incidence rate. Several noninvasive image techniques and computer-aided diagnosis systems have been developed to help find melanoma in its early stages. However, most previous research utilized dermoscopic images to build a diagnosis model, and only a few used prospective datasets. This study develops and evaluates a convolutional neural network (CNN) for melanoma identification and risk prediction using optical coherence tomography (OCT) imaging of mice skin. Longitudinal tests are performed on four animal models: melanoma mice, dysplastic nevus mice, and their respective controls. The CNN classifies melanoma and healthy tissues with high sensitivity (0.99) and specificity (0.98) and also assigns a risk score to each image based on the probability of melanoma presence, which may facilitate early diagnosis and management of melanoma in clinical settings.

Semantic Segmentation of the Prostate Based on Onefold and Joint Multimodal Medical Images Using YOLOv4 and U-Net

Magnetic Resonance Imaging is increasing in importance in prostate cancer diagnosis due to the high accuracy and quality of the examination procedure. However, this process requires a time-consuming analysis of the results. Currently, machine vision is widely used in many areas. It enables automation and support in radiological studies. Successful detection of primary prostate tumors depends on the effective segmentation of the prostate itself. At times, a CT scan may be performed; alternatively, MRI may be the selected option. The data always reach a bottleneck stage. This paper presents the effective training of deep learning models to segment the prostate based on onefold and multimodal medical images. This approach supports the computer-aided diagnosis (CAD) system for radiologists as the first step in cancer exams. A comparison of two approaches designed for prostate segmentation is described. The first combines YOLOv4, the object detection neural network, and U-Net for a semantic segmentation based on onefold modality MRI images. The second presents the same method trained on multimodal images—a CT and MRI mixed dataset. The learning process was carried out in a cloud environment using GPU cards. The experiments are based on data from 120 patients who have undergone MRI and CT examinations. Several metrics evaluated the trained models. In the prostate semantic segmentation process, better results were achieved by mixed MRI with CT datasets. The best model achieved the value of 0.9685 for the Sørensen–Dice coefficient for the threshold value of 0.6.

Diagnosis of COVID-19 from computed tomography slices using flower pollination algorithm, k-nearest neighbor, and support vector machine classifiers

Coronavirus disease 19 (COVID-19), caused by the severe acute respiratory syndrome-coronavirus-2 virus, is commonly diagnosed through imaging techniques such as computed tomography (CT) scans, which reveal characteristic lung lesions. In this study, we propose a computer-aided diagnosis (CAD) system to assist in the early detection of COVID-19 from CT lung slices, leveraging advanced machine-learning algorithms for precise and efficient analysis. To achieve this, we developed a CAD system that diagnoses COVID-19 from CT lung slices. An adaptive Wiener filter was applied to remove noise from the CT images. The chest tissues were then segmented using an optimal thresholding method to extract regions of interest, which represent the COVID-19 lesions under investigation. The feature vectors were divided into training and testing with an 80/20 ratio. A wrapper-based flower pollination algorithm was employed alongside the k-nearest neighbor classifier to select the optimal feature set. These selected features were subsequently used to train a support vector machine (SVM) classifier. With feature selection, the SVM achieved an accuracy of 91.30% on a real-time dataset, outperforming seven other machine learning classifiers (radial basis function-SVM, k nearest neighbor, linear discriminant analysis, random forest, na&amp;iuml;ve Bayes, AdaBoost, extreme gradient boosting) and four deep learning classifiers (convolutional neural network, recurrent neural network, long short term memory, Bidirectional long short term memory). For the publicly available COVID-19 CT dataset, an accuracy of 88.18% was achieved. In conclusion, our COVID-19 CAD system improves diagnostic accuracy, with future work aimed at enhancing efficiency and expanding to covariant detection and severity assessment.

Computer-aided diagnosis of liver cancer with improved SegNet and deep stacking ensemble model

Liver cancer is a leading cause of cancer-related deaths, often diagnosed at advanced stages due to reliance on traditional imaging methods. Existing computer-aided diagnosis systems struggle with noise, anatomical complexity, and ineffective feature integration, leading to inaccuracies in lesion segmentation and classification. By effectively addressing these challenges, the model aims to enhance early detection and assist clinicians in making informed decisions. Ultimately, this research seeks to contribute to more efficient and accurate liver cancer diagnosis. This paper presents a novel model for liver cancer classification, called SegNet-based Liver Cancer Classification via SqueezeNet (SgN-LCC-SqN). The model effectively executes liver cancer segmentation and classification through four key steps: preprocessing, segmentation, feature extraction, and classification. During preprocessing, Quadratic Mean Estimated Wiener Filtering (QMEWF) is utilized to minimize image noise. Segmentation divides the image into segments using Enhanced Feature Pyramid SegNet (EFP-SgN), which is essential for precise diagnosis. Feature extraction encompasses color features, Local Directional Pattern Variance, and Correlation Filtering-Local Gradient Increasing Pattern (CF-LGIP) features. The extracted features are then processed through an ensemble model, Deep Convolutional, Recurrent, Long Short Term Memory with SqueezeNet (DCR-LSTM-SqN), which includes Deep Convolutional Neural Network (DCNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Modified Loss Function in SqueezeNet (MLF-SqN) classifiers, sequentially analyzing the feature sets through DCNN, RNN, and LSTM before classification by MLF-SqN. The performance of the suggested DCR-LSTM-SqN model is evaluated over conventional methods for positive, negative and other metrics. The DCR-LSTM-SqN model consistently demonstrates superior accuracy, ranging from 0.947 to 0.984, across all training data percentages. Thus, the proposed model effectively segments liver lesions and classifies cancerous areas, demonstrating its potential as a valuable resource for clinicians to enhance the efficiency and accuracy of liver cancer diagnosis.