Computer-assisted Diagnostic Systems Research Articles

LightCF-Net: A Lightweight Long-Range Context Fusion Network for Real-Time Polyp Segmentation.

Automatically segmenting polyps from colonoscopy videos is crucial for developing computer-assisted diagnostic systems for colorectal cancer. Existing automatic polyp segmentation methods often struggle to fulfill the real-time demands of clinical applications due to their substantial parameter count and computational load, especially those based on Transformer architectures. To tackle these challenges, a novel lightweight long-range context fusion network, named LightCF-Net, is proposed in this paper. This network attempts to model long-range spatial dependencies while maintaining real-time performance, to better distinguish polyps from background noise and thus improve segmentation accuracy. A novel Fusion Attention Encoder (FAEncoder) is designed in the proposed network, which integrates Large Kernel Attention (LKA) and channel attention mechanisms to extract deep representational features of polyps and unearth long-range dependencies. Furthermore, a newly designed Visual Attention Mamba module (VAM) is added to the skip connections, modeling long-range context dependencies in the encoder-extracted features and reducing background noise interference through the attention mechanism. Finally, a Pyramid Split Attention module (PSA) is used in the bottleneck layer to extract richer multi-scale contextual features. The proposed method was thoroughly evaluated on four renowned polyp segmentation datasets: Kvasir-SEG, CVC-ClinicDB, BKAI-IGH, and ETIS. Experimental findings demonstrate that the proposed method delivers higher segmentation accuracy in less time, consistently outperforming the most advanced lightweight polyp segmentation networks.

A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review

BackgroundLung diseases, both infectious and non-infectious, are the most prevalent cause of mortality overall in the world. Medical research has identified pneumonia, lung cancer, and Corona Virus Disease 2019 (COVID-19) as prominent lung diseases prioritized over others. Imaging modalities, including X-rays, computer tomography (CT) scans, magnetic resonance imaging (MRIs), positron emission tomography (PET) scans, and others, are primarily employed in medical assessments because they provide computed data that can be utilized as input datasets for computer-assisted diagnostic systems. Imaging datasets are used to develop and evaluate machine learning (ML) methods to analyze and predict prominent lung diseases.ObjectiveThis review analyzes ML paradigms, imaging modalities' utilization, and recent developments for prominent lung diseases. Furthermore, the research also explores various datasets available publically that are being used for prominent lung diseases.MethodsThe well-known databases of academic studies that have been subjected to peer review, namely ScienceDirect, arXiv, IEEE Xplore, MDPI, and many more, were used for the search of relevant articles. Applied keywords and combinations used to search procedures with primary considerations for review, such as pneumonia, lung cancer, COVID-19, various imaging modalities, ML, convolutional neural networks (CNNs), transfer learning, and ensemble learning.ResultsThis research finding indicates that X-ray datasets are preferred for detecting pneumonia, while CT scan datasets are predominantly favored for detecting lung cancer. Furthermore, in COVID-19 detection, X-ray datasets are prioritized over CT scan datasets. The analysis reveals that X-rays and CT scans have surpassed all other imaging techniques. It has been observed that using CNNs yields a high degree of accuracy and practicability in identifying prominent lung diseases. Transfer learning and ensemble learning are complementary techniques to CNNs to facilitate analysis. Furthermore, accuracy is the most favored metric for assessment.

A Framework for Detecting Thyroid Cancer from Ultrasound and Histopathological Images Using Deep Learning, Meta-Heuristics, and MCDM Algorithms.

Computer-assisted diagnostic systems have been developed to aid doctors in diagnosing thyroid-related abnormalities. The aim of this research is to improve the diagnosis accuracy of thyroid abnormality detection models that can be utilized to alleviate undue pressure on healthcare professionals. In this research, we proposed deep learning, metaheuristics, and a MCDM algorithms-based framework to detect thyroid-related abnormalities from ultrasound and histopathological images. The proposed method uses three recently developed deep learning techniques (DeiT, Swin Transformer, and Mixer-MLP) to extract features from the thyroid image datasets. The feature extraction techniques are based on the Image Transformer and MLP models. There is a large number of redundant features that can overfit the classifiers and reduce the generalization capabilities of the classifiers. In order to avoid the overfitting problem, six feature transformation techniques (PCA, TSVD, FastICA, ISOMAP, LLE, and UMP) are analyzed to reduce the dimensionality of the data. There are five different classifiers (LR, NB, SVC, KNN, and RF) evaluated using the 5-fold stratified cross-validation technique on the transformed dataset. Both datasets exhibit large class imbalances and hence, the stratified cross-validation technique is used to evaluate the performance. The MEREC-TOPSIS MCDM technique is used for ranking the evaluated models at different analysis stages. In the first stage, the best feature extraction and classification techniques are chosen, whereas, in the second stage, the best dimensionality reduction method is evaluated in wrapper feature selection mode. Two best-ranked models are further selected for the weighted average ensemble learning and features selection using the recently proposed meta-heuristics FOX-optimization algorithm. The PCA+FOX optimization-based feature selection + random forest model achieved the highest TOPSIS score and performed exceptionally well with an accuracy of 99.13%, F2-score of 98.82%, and AUC-ROC score of 99.13% on the ultrasound dataset. Similarly, the model achieved an accuracy score of 90.65%, an F2-score of 92.01%, and an AUC-ROC score of 95.48% on the histopathological dataset. This study exploits the combination novelty of different algorithms in order to improve the thyroid cancer diagnosis capabilities. This proposed framework outperforms the current state-of-the-art diagnostic methods for thyroid-related abnormalities in ultrasound and histopathological datasets and can significantly aid medical professionals by reducing the excessive burden on the medical fraternity.

Rhode Island gastroenterology video capsule endoscopy data set

Complete endoscopic evaluation of the small bowel is challenging due to its length and anatomy. Although several advances have been made to achieve diagnostic and therapeutic goals, including double-balloon enteroscopy, single-balloon enteroscopy, and spiral enteroscopy, video capsule endoscopy (VCE) remains the least invasive tool for complete visualization of the small bowel and is the preferred method for initial diagnostic evaluation. At present, interpretation of VCE data requires manual annotation of landmarks and abnormalities in recorded videos, which can be time consuming. Computer-assisted diagnostic systems using artificial intelligence may help to optimize VCE reading efficiency by reducing the need for manual annotation. Here we present a large VCE data set compiled from studies performed at two United States hospitals in Providence, Rhode Island, including 424 VCE studies and 5,247,588 total labeled images. In conjunction with existing published data sets, these files may aid in the development of algorithms to further improve VCE.

Automatic Classification of Foot Thermograms Using Machine Learning Techniques

Diabetic foot is one of the main complications observed in diabetic patients; it is associated with the development of foot ulcers and can lead to amputation. In order to diagnose these complications, specialists have to analyze several factors. To aid their decisions and help prevent mistakes, the resort to computer-assisted diagnostic systems using artificial intelligence techniques is gradually increasing. In this paper, two different models for the classification of thermograms of the feet of diabetic and healthy individuals are proposed and compared. In order to detect and classify abnormal changes in the plantar temperature, machine learning algorithms are used in both models. In the first model, the foot thermograms are classified into four classes: healthy and three categories for diabetics. The second model has two stages: in the first stage, the foot is classified as belonging to a diabetic or healthy individual, while, in the second stage, a classification refinement is conducted, classifying diabetic foot into three classes of progressive severity. The results show that both proposed models proved to be efficient, allowing us to classify a foot thermogram as belonging to a healthy or diabetic individual, with the diabetic ones divided into three classes; however, when compared, Model 2 outperforms Model 1 and allows for a better performance classification concerning the healthy category and the first class of diabetic individuals. These results demonstrate that the proposed methodology can be a tool to aid medical diagnosis.

Automated multi-class skin cancer classification through concatenated deep learning models

Skin cancer is the most annoying type of cancer diagnosis according to its fast spread to various body areas, so it was necessary to establish computer-assisted diagnostic support systems. State-of-the-art classifiers based on convolutional neural networks (CNNs) are used to classify images of skin cancer. This paper tries to get the most accurate model to classify and detect skin cancer types from seven different classes using deep learning techniques; ResNet-50, VGG-16, and the merged model of these two techniques through the concatenate function. The performance of the proposed model was evaluated through a set of experiments on the HAM10000 database. The proposed system has succeeded in achieving a recognition accuracy of up to 94.14%.

Detection of Breast Cancer with Lightweight Deep Neural Networks for Histology Image Classification.

Many researchers have developed computer-assisted diagnostic (CAD) methods to diagnose breast cancer using histopathology microscopic images. These techniques help to improve the accuracy of biopsy diagnosis with hematoxylin and eosin-stained images. On the other hand, most CAD systems usually rely on inefficient and time-consuming manual feature extraction methods. Using a deep learning (DL) model with convolutional layers, we present a method to extract the most useful pictorial information for breast cancer classification. Breast biopsy images stained with hematoxylin and eosin can be categorized into four groups namely benign lesions, normal tissue, carcinoma in situ, and invasive carcinoma. To correctly classify different types of breast cancer, it is important to classify histopathological images accurately. The MobileNet architecture model is used to obtain high accuracy with less resource utilization. The proposed model is fast, inexpensive, and safe due to which it is suitable for the detection of breast cancer at an early stage. This lightweight deep neural network can be accelerated using field-programmable gate arrays for the detection of breast cancer. DL has been implemented to successfully classify breast cancer. The model uses categorical cross-entropy to learn to give the correct class a high probability and other classes a low probability. It is used in the classification stage of the convolutional neural network (CNN) after the clustering stage, thereby improving the performance of the proposed system. To measure training and validation accuracy, the model was trained on Google Colab for 280 epochs with a powerful GPU with 2496 CUDA cores, 12 GB GDDR5 VRAM, and 12.6 GB RAM. Our results demonstrate that deep CNN with a chi-square test has improved the accuracy of histopathological image classification of breast cancer by greater than 11% compared with other state-of-the-art methods.

Comparison of Multi-Label U-Net and Mask R-CNN for panoramic radiograph segmentation to detect periodontitis.

Periodontitis, the most prevalent chronic inflammatory condition affecting teeth-supporting tissues, is diagnosed and classified through clinical and radiographic examinations. The staging of periodontitis using panoramic radiographs provides information for designing computer-assisted diagnostic systems. Performing image segmentation in periodontitis is required for image processing in diagnostic applications. This study evaluated image segmentation for periodontitis staging based on deep learning approaches. Multi-Label U-Net and Mask R-CNN models were compared for image segmentation to detect periodontitis using 100 digital panoramic radiographs. Normal conditions and 4 stages of periodontitis were annotated on these panoramic radiographs. A total of 1100 original and augmented images were then randomly divided into a training (75%) dataset to produce segmentation models and a testing (25%) dataset to determine the evaluation metrics of the segmentation models. The performance of the segmentation models against the radiographic diagnosis of periodontitis conducted by a dentist was described by evaluation metrics (i.e., dice coefficient and intersection-over-union [IoU] score). Multi-Label U-Net achieved a dice coefficient of 0.96 and an IoU score of 0.97. Meanwhile, Mask R-CNN attained a dice coefficient of 0.87 and an IoU score of 0.74. U-Net showed the characteristic of semantic segmentation, and Mask R-CNN performed instance segmentation with accuracy, precision, recall, and F1-score values of 95%, 85.6%, 88.2%, and 86.6%, respectively. Multi-Label U-Net produced superior image segmentation to that of Mask R-CNN. The authors recommend integrating it with other techniques to develop hybrid models for automatic periodontitis detection.

Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods.

The main cause of death in Mexico and the world is heart disease, and it will continue to lead the death rate in the next decade according to data from the World Health Organization (WHO) and the National Institute of Statistics and Geography (INEGI). Therefore, the objective of this work is to implement, compare and evaluate machine learning algorithms that are capable of classifying normal and abnormal heart sounds. Three different sounds were analyzed in this study; normal heart sounds, heart murmur sounds and extra systolic sounds, which were labeled as healthy sounds (normal sounds) and unhealthy sounds (murmur and extra systolic sounds). From these sounds, fifty-two features were calculated to create a numerical dataset; thirty-six statistical features, eight Linear Predictive Coding (LPC) coefficients and eight Cepstral Frequency-Mel Coefficients (MFCC). From this dataset two more were created; one normalized and one standardized. These datasets were analyzed with six classifiers: k-Nearest Neighbors, Naive Bayes, Decision Trees, Logistic Regression, Support Vector Machine and Artificial Neural Networks, all of them were evaluated with six metrics: accuracy, specificity, sensitivity, ROC curve, precision and F1-score, respectively. The performances of all the models were statistically significant, but the models that performed best for this problem were logistic regression for the standardized data set, with a specificity of 0.7500 and a ROC curve of 0.8405, logistic regression for the normalized data set, with a specificity of 0.7083 and a ROC curve of 0.8407, and Support Vector Machine with a lineal kernel for the non-normalized data; with a specificity of 0.6842 and a ROC curve of 0.7703. Both of these metrics are of utmost importance in evaluating the performance of computer-assisted diagnostic systems.

Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy.

Diabetic retinopathy (DR) is characterized by the presence of red lesions (RLs), such as microaneurysms and hemorrhages, and bright lesions, such as exudates (EXs). Early DR diagnosis is paramount to prevent serious sight damage. Computer-assisted diagnostic systems are based on the detection of those lesions through the analysis of fundus images. In this paper, a novel method is proposed for the automatic detection of RLs and EXs. As the main contribution, the fundus image was decomposed into various layers, including the lesion candidates, the reflective features of the retina, and the choroidal vasculature visible in tigroid retinas. We used a proprietary database containing 564 images, randomly divided into a training set and a test set, and the public database DiaretDB1 to verify the robustness of the algorithm. Lesion detection results were computed per pixel and per image. Using the proprietary database, 88.34% per-image accuracy (ACCi), 91.07% per-pixel positive predictive value (PPVp), and 85.25% per-pixel sensitivity (SEp) were reached for the detection of RLs. Using the public database, 90.16% ACCi, 96.26% PPV_p, and 84.79% SEp were obtained. As for the detection of EXs, 95.41% ACCi, 96.01% PPV_p, and 89.42% SE_p were reached with the proprietary database. Using the public database, 91.80% ACCi, 98.59% PPVp, and 91.65% SEp were obtained. The proposed method could be useful to aid in the diagnosis of DR, reducing the workload of specialists and improving the attention to diabetic patients.

RSESLIBKNN MAKİNE ÖĞRENMESİ YÖNTEMİ KULLANILARAK PARKİNSON HASTALIĞININ TANISI

Parkinson's disease is a neurological disease that affects the quality of life of people. Parkinson's disease is a disease that negatively affects the central nervous system. It can lead to death of patients. Therefore, early detection of Parkinson's disease is extremely important. Symptoms of Parkinson's disease can be detected with computer-assisted diagnostic systems based on potentially advanced machine learning techniques. In this study, kNN, RseslibKnn and A1DE machine learning methods were used for the diagnosis of Parkinson's disease. The aim of the study was to compare the success rates of the machine learning methods on the Parkinson's disease dataset and to present the most appropriate decision support system. The data set was composed of 252 samples and 753 attributes from the ‘UC Irvine Machine Learning Repository’ database. Different studies on the literature are also examined and compared. Experimental studies were conducted on different cross-validations and the average of these studies was given as a result of success. At the end of the study, the Parkinson's disease data set was classified with kNN, RseslibKnn and A1DE machine learning methods and then the training and test results were evaluated based on accuracy, sensitivity and specificity values. When all the methods examined with different cross validity values were examined, RseslibKnn method gave the highest success result with an average of %97,61 accuracy rate. As a result of the evaluation, the recommendations of the RseslibKnn machine learning method on decision support systems for the detection of Parkinson's disease were made.

The Impact of Artificial Intelligence in the Endoscopic Assessment of Premalignant and Malignant Esophageal Lesions: Present and Future.

In the gastroenterology field, the impact of artificial intelligence was investigated for the purposes of diagnostics, risk stratification of patients, improvement in quality of endoscopic procedures and early detection of neoplastic diseases, implementation of the best treatment strategy, and optimization of patient prognosis. Computer-assisted diagnostic systems to evaluate upper endoscopy images have recently emerged as a supporting tool in endoscopy due to the risks of misdiagnosis related to standard endoscopy and different expertise levels of endoscopists, time-consuming procedures, lack of availability of advanced procedures, increasing workloads, and development of endoscopic mass screening programs. Recent research has tended toward computerized, automatic, and real-time detection of lesions, which are approaches that offer utility in daily practice. Despite promising results, certain studies might overexaggerate the diagnostic accuracy of artificial systems, and several limitations remain to be overcome in the future. Therefore, additional multicenter randomized trials and the development of existent database platforms are needed to certify clinical implementation. This paper presents an overview of the literature and the current knowledge of the usefulness of different types of machine learning systems in the assessment of premalignant and malignant esophageal lesions via conventional and advanced endoscopic procedures. This study makes a presentation of the artificial intelligence terminology and refers also to the most prominent recent research on computer-assisted diagnosis of neoplasia on Barrett’s esophagus and early esophageal squamous cell carcinoma, and prediction of invasion depth in esophageal neoplasms. Furthermore, this review highlights the main directions of future doctor–computer collaborations in which machines are expected to improve the quality of medical action and routine clinical workflow, thus reducing the burden on physicians.

An Accurate and Fast Cardio-Views Classification System Based on Fused Deep Features and LSTM

Echocardiography is an ultrasound-based imaging modality that helps the physician to visualize heart chambers and valves motion activity. Recently, deep learning plays an important role in several clinical computer-assisted diagnostic systems. There is a real need to employ deep learning methodologies to increase such systems. In this paper, we proposed a deep learning system to classify several echocardiography views and identify its physiological location. Firstly, the spatial CNN features are extracted from each frame in the echo-motion. Secondly, we proposed novel temporal features based on neutrosophic sets. The neutrosophic temporal motion features are extracted from echo-motion activity. To extract the deep CNN features, we activated a pre-trained deep ResNet model. Then, both spatial and neutrosophic temporal CNN features were fused based on features concatenation technique. Finally, the fused CNN features were fed into deep long short-term memory network to classify echo-cardio views and identify their location. During our experiments, we employed a public echocardiography dataset that consisted of 432 videos for eight cardio-views. We have investigated several pre-trained network activation performance. ResNet architecture activation achieved the best accuracy score among several pre-trained networks. The Proposed system based on fused spatial neutrosophic temporal deep features achieved 96.3% accuracy and 95.75% sensitivity. For the classification of cardio-views location, the proposed system achieved 99.1% accuracy. The proposed system achieved more accuracy than previous deep learning methods with a significant decrease in the training time cost. The experimental results showed promising results for our proposed approach.

Diagnostic Classification of Cervical Cell Images from Pap Smear Slides

Analysis of pap smear images under a microscope by experts is a laborious and time consuming task. Computer-assisted diagnostic (CAD) systems can simplify this tedious process and allow experts to focus on more critical cases. Effective screening and early diagnosis can help to detect precancerous cells and allow early treatment. In this study, we have used a deep learning approach for classification of cervical cell images which obtained from pap smear slides. The proposed method automatically classifies cervical cell images into five categories without using any pre-processing on raw input images. We have obtained the promising results as compared to the previous studies in the literature. The proposed model can give a second opinion to clinicians in their daily routines and help them to focus on more complex cases.

Better diagnostic performance using computer-assisted diagnostic support systems in internal medicine

The recent application of artificial intelligence（AI）to clinical medicine has confirmed the usefulness of AI for diagnostic imaging, histopathological examinations, and dermatologic screening. Clinical decision support systems are another promising area to which AI could contribute toward better clinical decisions. We have developed computer-assisted diagnostic support systems to reduce human diagnostic errors such as delayed diagnoses, misdiagnoses, and overdiagnoses. Our three Diagnosis Reminder（DR）systems include two AI systems that use machine learning in their diagnosis algorithms. Here, we compared the diagnostic accuracy of a DR-supported group with that of an unassisted physicians group, using three difficult patient cases provided by experts in general medicine. Our analyses revealed that the three AI diagnostic systems could not provide accurate differential diagnoses up to top 10 in all three patient cases because of incomplete data inputs for machine learning. However, the first DR system, which was developed by an experienced diagnostician over the last 35 years, showed very useful performance in reducing human diagnostic errors when it was used by an expert physician. The use of AI diagnostic systems by knowledgeable physicians will lead to better diagnostic performance. We also discuss the current scenario, future challenges, and prospects for AI diagnostic systems herein.

Oretinex-DI: Pre-Processing Algorithms for Melanoma Image Enhancement

In Medical imaging, the dermoscopic images analysis is quite useful for the skin cancer detection. The automatic computer assisted diagnostic systems (CADS) require dermoscopic image enhancement for human perception and analysis. The traditional image enhancements methods lack the synchronization among contrast perception between human and the digital images. This paper proposes an optimized-Retinex (ORetinex) image enhancement algorithm to remove light effects, which is quite suitable for the dermoscopic image for clinical analysis for Melanoma. The value of global contrast factor (GCF) and contrast per pixel (CPP) is computed and compared with the traditional methods of image enhancements including contrast enhancement, CLAHE,Adaptive histogram equalization, Bilinear filtering and the proportion of GCF and CPP is found quite optimal as compare to these traditional methods.

Development of a diagnostic decision tree for obstructive pulmonary diseases based on real-life data.

The aim of this study was to develop and explore the diagnostic accuracy of a decision tree derived from a large real-life primary care population.Data from 9297 primary care patients (45% male, mean age 53±17 years) with suspicion of an obstructive pulmonary disease was derived from an asthma/chronic obstructive pulmonary disease (COPD) service where patients were assessed using spirometry, the Asthma Control Questionnaire, the Clinical COPD Questionnaire, history data and medication use. All patients were diagnosed through the Internet by a pulmonologist. The Chi-squared Automatic Interaction Detection method was used to build the decision tree. The tree was externally validated in another real-life primary care population (n=3215).Our tree correctly diagnosed 79% of the asthma patients, 85% of the COPD patients and 32% of the asthma–COPD overlap syndrome (ACOS) patients. External validation showed a comparable pattern (correct: asthma 78%, COPD 83%, ACOS 24%).Our decision tree is considered to be promising because it was based on real-life primary care patients with a specialist's diagnosis. In most patients the diagnosis could be correctly predicted. Predicting ACOS, however, remained a challenge. The total decision tree can be implemented in computer-assisted diagnostic systems for individual patients. A simplified version of this tree can be used in daily clinical practice as a desk tool.

Development of a diagnostic and remedial learning system based on an enhanced concept–effect model

With the rapid advances in computer technology during recent years, researchers have demonstrated the pivotal influences of computer-assisted diagnostic systems on student learning performance improvement. This research aims to develop a Diagnostic and Remedial Learning System (DRLS) for an algebra course in a Thai lower secondary school context based on an enhanced learning diagnosis model which employs a novel technique to preset weight of association concepts for each test item and the learning preference style of individual students to advise the students how to improve their learning performance. To evaluate the effectiveness of this innovative approach, an experiment has been conducted on an algebra course in a Thai secondary school. Pre- and post-test, questionnaire and interview data have revealed that the suggestions provided by the system could help the students improve their learning performance.

Computer-assisted diagnostic decision support: history, challenges, and possible paths forward

This paper presents a brief history of computer-assisted diagnosis, including challenges and future directions. Some ideas presented in this article on computer-assisted diagnostic decision support systems (CDDSS) derive from prior work by the author and his colleagues (see list in Acknowledgments) on the INTERNIST-1 and QMR projects. References indicate the original sources of many of these ideas.

The Complexity of Diagnosing Melanoma

Recognizing that a cure lies in timely detection, dermatologists strive to diagnose malignant melanoma (MM) at the earliest possible stage. The desire to achieve this goal without injudiciously and unnecessarily excising many benign lesions has led to numerous techniques that assist clinicians in differentiating nevi from MM, including clinical mnemonics and algorithms, optical imaging instruments, and computer-assisted diagnostic systems. Most of these seemingly diverse methods rely on evaluating the in vivo morphology of lesions. In this issue, Guitera et al. compare dermoscopy with reflectance confocal microscopy (RCM) in an attempt to determine which imaging modality facilitates accurate diagnosis of melanocytic lesions using diagnostic parameters such as sensitivity and specificity.