DL-CNN-based approach with image processing techniques for diagnosis of retinal diseases

Effect of training sample size, image resolution and epochs on filamentous and floc-forming bacteria classification using machine learning

Computer vision techniques can expedite the detection of bacterial growth in wastewater treatment plants and alleviate some of the shortcomings associated with traditional detection methods. In recent years, researchers capitalized on this potential by developing segmentation algorithms that were specifically tailored to identify the overgrowth of filamentous bacteria and the risk of sludge bulking. This study investigated the optimization of an artificial intelligence (AI) segmentation model in terms of accuracy metrics and computational requirements. Specifically, three model variables were tested, including training sample size, image resolution, and number of training epochs. The results indicated that larger sample sizes resulted in higher output accuracy up to a certain limit (300 images), beyond which no significant improvements were observed. High image resolution (788 × 530) provided more details for the deep learning model to detect the fine edges between bacteria albeit with significant additional computational requirements. The addition of more training epochs resulted in a minor increase in segmentation accuracy, particularly for thin interconnected filamentous bacteria. Overall, high resolution and epochs did not have a major effect when the sample size was large (300 and 500 images). The findings highlight the optimal balance between model accuracy and computational demands, emphasizing the importance of prioritizing diverse training samples with sufficient sample size. This approach is critical for large-scale implementation, as it enhances the potential of AI to deliver timely and accurate predictions, leading to early warnings of wastewater treatment issues such as sludge bulking.

A Low Complexity Efficient Deep Learning Model for Automated Retinal Disease Diagnosis

The identification and early treatment of retinal disease can help to prevent loss of vision. Early diagnosis allows a greater range of treatment options and results in better outcomes. Optical coherence tomography (OCT) is a technology used by ophthalmologists to detect and diagnose certain eye conditions. In this paper, human retinal OCT images are classified into four classes using deep learning. Several image preprocessing techniques are employed to enhance the image quality. An augmentation technique, called generative adversarial network (GAN), is utilized in the Drusen and DME classes to address data imbalance issues, resulting in a total of 130,649 images. A lightweight optimized compact convolutional transformers (OCCT) model is developed by conducting an ablation study on the initial CCT model for categorizing retinal conditions. The proposed OCCT model is compared with two transformer-based models: vision Transformer (ViT) and Swin Transformer. The models are trained and evaluated with 32 × 32 sized images of the GAN-generated enhanced dataset. Additionally, eight transfer learning models are presented with the same input images to compare their performance with the OCCT model. The proposed model's stability is assessed by decreasing the number of training images and evaluating the performance. The OCCT model's accuracy is 97.09%, and it outperforms the two transformer models. The result further indicates that the OCCT model sustains its performance, even if the number of images is reduced.

Combination of Deep and Statistical Features of the Tissue of Pathology Images to Classify and Diagnose the Degree of Malignancy of Prostate Cancer

Prostate cancer is one of the most prevalent male-specific diseases, where early and accurate diagnosis is essential for effective treatment and preventing disease progression. Assessing disease severity involves analyzing histological tissue samples, which are graded from 1 (healthy) to 5 (severely malignant) based on pathological features. However, traditional manual grading is labor-intensive and prone to variability. This study addresses the challenge of automating prostate cancer classification by proposing a novel histological grade analysis approach. The method integrates the gray-level co-occurrence matrix (GLCM) for extracting texture features with Haar wavelet modification to enhance feature quality. A convolutional neural network (CNN) is then employed for robust classification. The proposed method was evaluated using statistical and performance metrics, achieving an average accuracy of 97.3%, a precision of 98%, and an AUC of 0.95. These results underscore the effectiveness of the approach in accurately categorizing prostate tissue grades. This study demonstrates the potential of automated classification methods to support pathologists, enhance diagnostic precision, and improve clinical outcomes in prostate cancer care.

Artificial intelligence for diagnosing exudative age-related macular degeneration

BACKGROUND

Age-related macular degeneration (AMD) is a retinal disorder characterized by central retinal (macular) damage. Approximately 10% to 20% of non-exudative AMD cases progress to the exudative form, which may result in rapid deterioration of central vision. Individuals with exudative AMD (eAMD) need prompt consultation with retinal specialists to minimize the risk and extent of vision loss. Traditional methods of diagnosing ophthalmic disease rely on clinical evaluation and multiple imaging techniques, which can be resource-consuming. Tests leveraging artificial intelligence (AI) hold the promise of automatically identifying and categorizing pathological features, enabling the timely diagnosis and treatment of eAMD.

OBJECTIVES

To determine the diagnostic accuracy of artificial intelligence (AI) as a triaging tool for exudative age-related macular degeneration (eAMD).

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, three clinical trials registries, and Data Archiving and Networked Services (DANS) for gray literature. We did not restrict searches by language or publication date. The date of the last search was April 2024.

SELECTION CRITERIA

Included studies compared the test performance of algorithms with that of human readers to detect eAMD on retinal images collected from people with AMD who were evaluated at eye clinics in community or academic medical centers, and who were not receiving treatment for eAMD when the images were taken. We included algorithms that were either internally or externally validated or both.

DATA COLLECTION AND ANALYSIS

Pairs of review authors independently extracted data and assessed study quality using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool with revised signaling questions. For studies that reported more than one set of performance results, we extracted only one set of diagnostic accuracy data per study based on the last development stage or the optimal algorithm as indicated by the study authors. For two-class algorithms, we collected data from the 2x2 table whenever feasible. For multi-class algorithms, we first consolidated data from all classes other than eAMD before constructing the corresponding 2x2 tables. Assuming a common positivity threshold applied by the included studies, we chose random-effects, bivariate logistic models to estimate summary sensitivity and specificity as the primary performance metrics.

MAIN RESULTS

We identified 36 eligible studies that reported 40 sets of algorithm performance data, encompassing over 16,000 participants and 62,000 images. We included 28 studies (78%) that reported 31 algorithms with performance data in the meta-analysis. The remaining nine studies (25%) reported eight algorithms that lacked usable performance data; we reported them in the qualitative synthesis. Study characteristics and risk of bias Most studies were conducted in Asia, followed by Europe, the USA, and collaborative efforts spanning multiple countries. Most studies identified study participants from the hospital setting, while others used retinal images from public repositories; a few studies did not specify image sources. Based on four of the 36 studies reporting demographic information, the age of the study participants ranged from 62 to 82 years. The included algorithms used various retinal image types as model input, such as optical coherence tomography (OCT) images (N = 15), fundus images (N = 6), and multi-modal imaging (N = 7). The predominant core method used was deep neural networks. All studies that reported externally validated algorithms were at high risk of bias mainly due to potential selection bias from either a two-gate design or the inappropriate exclusion of potentially eligible retinal images (or participants). Findings Only three of the 40 included algorithms were externally validated (7.5%, 3/40). The summary sensitivity and specificity were 0.94 (95% confidence interval (CI) 0.90 to 0.97) and 0.99 (95% CI 0.76 to 1.00), respectively, when compared to human graders (3 studies; 27,872 images; low-certainty evidence). The prevalence of images with eAMD ranged from 0.3% to 49%. Twenty-eight algorithms were reportedly either internally validated (20%, 8/40) or tested on a development set (50%, 20/40); the pooled sensitivity and specificity were 0.93 (95% CI 0.89 to 0.96) and 0.96 (95% CI 0.94 to 0.98), respectively, when compared to human graders (28 studies; 33,409 images; low-certainty evidence). We did not identify significant sources of heterogeneity among these 28 algorithms. Although algorithms using OCT images appeared more homogeneous and had the highest summary specificity (0.97, 95% CI 0.93 to 0.98), they were not superior to algorithms using fundus images alone (0.94, 95% CI 0.89 to 0.97) or multimodal imaging (0.96, 95% CI 0.88 to 0.99; P for meta-regression = 0.239). The median prevalence of images with eAMD was 30% (interquartile range [IQR] 22% to 39%). We did not include eight studies that described nine algorithms (one study reported two sets of algorithm results) to distinguish eAMD from normal images, images of other AMD, or other non-AMD retinal lesions in the meta-analysis. Five of these algorithms were generally based on smaller datasets (range 21 to 218 participants per study) yet with a higher prevalence of eAMD images (range 33% to 66%). Relative to human graders, the reported sensitivity in these studies ranged from 0.95 and 0.97, while the specificity ranged from 0.94 to 0.99. Similarly, using small datasets (range 46 to 106), an additional four algorithms for detecting eAMD from other retinal lesions showed high sensitivity (range 0.96 to 1.00) and specificity (range 0.77 to 1.00).

AUTHORS' CONCLUSIONS

Low- to very low-certainty evidence suggests that an algorithm-based test may correctly identify most individuals with eAMD without increasing unnecessary referrals (false positives) in either the primary or the specialty care settings. There were significant concerns for applying the review findings due to variations in the eAMD prevalence in the included studies. In addition, among the included algorithm-based tests, diagnostic accuracy estimates were at risk of bias due to study participants not reflecting real-world characteristics, inadequate model validation, and the likelihood of selective results reporting. Limited quality and quantity of externally validated algorithms highlighted the need for high-certainty evidence. This evidence will require a standardized definition for eAMD on different imaging modalities and external validation of the algorithm to assess generalizability.

An efficient ANN SoC for detecting Alzheimer's disease based on recurrent computing

Alzheimer's Disease (AD) is an irreversible, degenerative condition that, while incurable, can have its progression slowed or impeded. While there are numerous methods utilizing neural networks for AD detection, there is a scarcity of High-performance AD detection chips. Moreover, excessively complex neural networks are not conducive to on-chip implementation and clinical applications. This study addresses the challenges of high misdiagnosis rates and significant hardware costs inherent in traditional AD detection techniques. A novel and efficient AD detection framework based on a recurrent computational strategy is proposed. The framework harnesses an Artificial Neural Network (ANN) embedded within a System on Chip (SoC) to perform sophisticated Electroencephalogram (EEG) analysis. The approach began by employing a reduced IEEE754 single-precision encoding method to hardware-encode the preprocessed EEG data, thereby minimizing the memory storage area. Next, data remapping techniques were utilized to ensure the continuity of the input data read addresses and reduce the memory access pressure during neural network computations. Subsequently, hierarchical and Processing Element (PE) reuse technologies were leveraged to perform the multiply-accumulate operations of the ANN. Finally, a step function was chosen to establish binary classification circuits dedicated to AD detection. Experimental results indicate that the optimized SoC achieves a 70 % reduction in area and a 50 % reduction in power consumption compared to traditional designs. For various neural network models, the detection model proposed in this paper incurs less overhead, with a training speed 3 to 4 times faster than other traditional models, and a high accuracy rate of 98.53 %.

Explainable AI (XAI) for Neonatal Pain Assessment via Influence Function Modification

As machine learning increasingly plays a crucial role in various medical applications, the need for improved explainability of these complex, often opaque models becomes more urgent. Influence functions have emerged as a critical method for explaining these black-box models. Influence functions measure how much each training sample affects a model's predictions on test data, with previous research indicating that the most influential training samples usually exhibit a high degree of semantic similarity to the test point. Building on this concept, we propose a novel approach that modifies the influence function for more precise influence estimations. This involves adding a new weighting factor to the influence function based on the similarity of the test and training data. We employ cosine similarity, Euclidean distance, and the structural similarity index to calculate this weight. The modified influence method is evaluated on a neonatal pain assessment model to explain the decision, revealing excellent performance in identifying influential training points compared to the baseline method. These results show the effectiveness of the proposed approach in elucidating the decision-making process.

A improved pooling method for convolutional neural networks

The pooling layer in convolutional neural networks plays a crucial role in reducing spatial dimensions, and improving computational efficiency. However, standard pooling operations such as max pooling or average pooling are not suitable for all applications and data types. Therefore, developing custom pooling layers that can adaptively learn and extract relevant features from specific datasets is of great significance. In this paper, we propose a novel approach to design and implement customizable pooling layers to enhance feature extraction capabilities in CNNs. The proposed T-Max-Avg pooling layer incorporates a threshold parameter T, which selects the K highest interacting pixels as specified, allowing it to control whether the output features of the input data are based on the maximum values or weighted averages. By learning the optimal pooling strategy during training, our custom pooling layer can effectively capture and represent discriminative information in the input data, thereby improving classification performance. Experimental results show that the proposed T-Max-Avg pooling layer achieves good performance on three different datasets. When compared to LeNet-5 model with average pooling, max pooling, and Avg-TopK methods, the T-Max-Avg pooling method achieves the highest accuracy on CIFAR-10, CIFAR-100, and MNIST datasets.

A fusion of deep neural networks and game theory for retinal disease diagnosis with OCT images

Retinal disorders pose a serious threat to world healthcare because they frequently result in visual loss or impairment. For retinal disorders to be diagnosed precisely, treated individually, and detected early, deep learning is a necessary subset of artificial intelligence. This paper provides a complete approach to improve the accuracy and reliability of retinal disease identification using images from OCT (Retinal Optical Coherence Tomography). The Hybrid Model GIGT, which combines Generative Adversarial Networks (GANs), Inception, and Game Theory, is a novel method for diagnosing retinal diseases using OCT pictures. This technique, which is carried out in Python, includes preprocessing images, feature extraction, GAN classification, and a game-theoretic examination. Resizing, grayscale conversion, noise reduction using Gaussian filters, contrast enhancement using Contrast Limiting Adaptive Histogram Equalization (CLAHE), and edge recognition via the Canny technique are all part of the picture preparation step. These procedures set up the OCT pictures for efficient analysis. The Inception model is used for feature extraction, which enables the extraction of discriminative characteristics from the previously processed pictures. GANs are used for classification, which improves accuracy and resilience by adding a strategic and dynamic aspect to the diagnostic process. Additionally, a game-theoretic analysis is utilized to evaluate the security and dependability of the model in the face of hostile attacks. Strategic analysis and deep learning work together to provide a potent diagnostic tool. This suggested model's remarkable 98.2% accuracy rate shows how this method has the potential to improve the detection of retinal diseases, improve patient outcomes, and address the worldwide issue of visual impairment.

Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals

Artificial intelligence tools, particularly convolutional neural networks (CNNs), are transforming healthcare by enhancing predictive, diagnostic, and decision-making capabilities. This review provides an accessible and practical explanation of CNNs for clinicians and highlights their relevance in medical image analysis. CNNs have shown themselves to be exceptionally useful in computer vision, a field that enables machines to 'see' and interpret visual data. Understanding how these models work can help clinicians leverage their full potential, especially as artificial intelligence continues to evolve and integrate into healthcare. CNNs have already demonstrated their efficacy in diverse medical fields, including radiology, histopathology, and medical photography. In radiology, CNNs have been used to automate the assessment of conditions such as pneumonia, pulmonary embolism, and rectal cancer. In histopathology, CNNs have been used to assess and classify colorectal polyps, gastric epithelial tumours, as well as assist in the assessment of multiple malignancies. In medical photography, CNNs have been used to assess retinal diseases and skin conditions, and to detect gastric and colorectal polyps during endoscopic procedures. In surgical laparoscopy, they may provide intraoperative assistance to surgeons, helping interpret surgical anatomy and demonstrate safe dissection zones. The integration of CNNs into medical image analysis promises to enhance diagnostic accuracy, streamline workflow efficiency, and expand access to expert-level image analysis, contributing to the ultimate goal of delivering further improvements in patient and healthcare outcomes.

Deep-Ocular: Improved Transfer Learning Architecture Using Self-Attention and Dense Layers for Recognition of Ocular Diseases

It is difficult for clinicians or less-experienced ophthalmologists to detect early eye-related diseases. By hand, eye disease diagnosis is labor-intensive, prone to mistakes, and challenging because of the variety of ocular diseases such as glaucoma (GA), diabetic retinopathy (DR), cataract (CT), and normal eye-related diseases (NL). An automated ocular disease detection system with computer-aided diagnosis (CAD) tools is required to recognize eye-related diseases. Nowadays, deep learning (DL) algorithms enhance the classification results of retinograph images. To address these issues, we developed an intelligent detection system based on retinal fundus images. To create this system, we used ODIR and RFMiD datasets, which included various retinographics of distinct classes of the fundus, using cutting-edge image classification algorithms like ensemble-based transfer learning. In this paper, we suggest a three-step hybrid ensemble model that combines a classifier, a feature extractor, and a feature selector. The original image features are first extracted using a pre-trained AlexNet model with an enhanced structure. The improved AlexNet (iAlexNet) architecture with attention and dense layers offers enhanced feature extraction, task adaptability, interpretability, and potential accuracy benefits compared to other transfer learning architectures, making it particularly suited for tasks like retinograph classification. The extracted features are then selected using the ReliefF method, and then the most crucial elements are chosen to minimize the feature dimension. Finally, an XgBoost classifier offers classification outcomes based on the desired features. These classifications represent different ocular illnesses. We utilized data augmentation techniques to control class imbalance issues. The deep-ocular model, based mainly on the AlexNet-ReliefF-XgBoost model, achieves an accuracy of 95.13%. The results indicate the proposed ensemble model can assist dermatologists in making early decisions for the diagnosing and screening of eye-related diseases.

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients' healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs' adaptation for complex applications. Specifically, we investigate ANNs' advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.

A bibliometric analysis of artificial intelligence applications in macular edema: exploring research hotspots and Frontiers

Background: Artificial intelligence (AI) is used in ophthalmological disease screening and diagnostics, medical image diagnostics, and predicting late-disease progression rates. We reviewed all AI publications associated with macular edema (ME) research Between 2011 and 2022 and performed modeling, quantitative, and qualitative investigations. Methods: On 1st February 2023, we screened the Web of Science Core Collection for AI applications related to ME, from which 297 studies were identified and analyzed (2011-2022). We collected information on: publications, institutions, country/region, keywords, journal name, references, and research hotspots. Literature clustering networks and Frontier knowledge bases were investigated using bibliometrix-BiblioShiny, VOSviewer, and CiteSpace bibliometric platforms. We used the R "bibliometrix" package to synopsize our observations, enumerate keywords, visualize collaboration networks between countries/regions, and generate a topic trends plot. VOSviewer was used to examine cooperation between institutions and identify citation relationships between journals. We used CiteSpace to identify clustering keywords over the timeline and identify keywords with the strongest citation bursts. Results: In total, 47 countries published AI studies related to ME; the United States had the highest H-index, thus the greatest influence. China and the United States cooperated most closely between all countries. Also, 613 institutions generated publications - the Medical University of Vienna had the highest number of studies. This publication record and H-index meant the university was the most influential in the ME field. Reference clusters were also categorized into 10 headings: retinal Optical Coherence Tomography (OCT) fluid detection, convolutional network models, deep learning (DL)-based single-shot predictions, retinal vascular disease, diabetic retinopathy (DR), convolutional neural networks (CNNs), automated macular pathology diagnosis, dry age-related macular degeneration (DARMD), class weight, and advanced DL architecture systems. Frontier keywords were represented by diabetic macular edema (DME) (2021-2022). Conclusion: Our review of the AI-related ME literature was comprehensive, systematic, and objective, and identified future trends and current hotspots. With increased DL outputs, the ME research focus has gradually shifted from manual ME examinations to automatic ME detection and associated symptoms. In this review, we present a comprehensive and dynamic overview of AI in ME and identify future research areas.

Deep ensemble learning for automated non-advanced AMD classification using optimized retinal layer segmentation and SD-OCT scans

BACKGROUND

Accurate retinal layer segmentation in optical coherence tomography (OCT) images is crucial for quantitatively analyzing age-related macular degeneration (AMD) and monitoring its progression. However, previous retinal segmentation models depend on experienced experts and manually annotating retinal layers is time-consuming. On the other hand, accuracy of AMD diagnosis is directly related to the segmentation model's performance. To address these issues, we aimed to improve AMD detection using optimized retinal layer segmentation and deep ensemble learning.

METHOD

We integrated a graph-cut algorithm with a cubic spline to automatically annotate 11 retinal boundaries. The refined images were fed into a deep ensemble mechanism that combined a Bagged Tree and end-to-end deep learning classifiers. We tested the developed deep ensemble model on internal and external datasets.

RESULTS

The total error rates for our segmentation model using the boundary refinement approach was significantly lower than OCT Explorer segmentations (1.7% vs. 7.8%, p-value = 0.03). We utilized the refinement approach to quantify 169 imaging features using Zeiss SD-OCT volume scans. The presence of drusen and thickness of total retina, neurosensory retina, and ellipsoid zone to inner-outer segment (EZ-ISOS) thickness had higher contributions to AMD classification compared to other features. The developed ensemble learning model obtained a higher diagnostic accuracy in a shorter time compared with two human graders. The area under the curve (AUC) for normal vs. early AMD was 99.4%.

CONCLUSION

Testing results showed that the developed framework is repeatable and effective as a potentially valuable tool in retinal imaging research.

Classification of Retinal Diseases in Optical Coherence Tomography Images Using Artificial Intelligence and Firefly Algorithm

In recent years, the number of studies for the automatic diagnosis of biomedical diseases has increased. Many of these studies have used Deep Learning, which gives extremely good results but requires a vast amount of data and computing load. If the processor is of insufficient quality, this takes time and places an excessive load on the processor. On the other hand, Machine Learning is faster than Deep Learning and does not have a much-needed computing load, but it does not provide as high an accuracy value as Deep Learning. Therefore, our goal is to develop a hybrid system that provides a high accuracy value, while requiring a smaller computing load and less time to diagnose biomedical diseases such as the retinal diseases we chose for this study. For this purpose, first, retinal layer extraction was conducted through image preprocessing. Then, traditional feature extractors were combined with pre-trained Deep Learning feature extractors. To select the best features, we used the Firefly algorithm. In the end, multiple binary classifications were conducted instead of multiclass classification with Machine Learning classifiers. Two public datasets were used in this study. The first dataset had a mean accuracy of 0.957, and the second dataset had a mean accuracy of 0.954.

Deep Residual Network for Diagnosis of Retinal Diseases Using Optical Coherence Tomography Images

Diabetic retinopathy occurs due to damage to the blood vessels in the retina, and it is a major health problem in recent years that progresses slowly without recognizable symptoms. Optical coherence tomography (OCT) is a popular and widely used noninvasive imaging modality for the diagnosis of diabetic retinopathy. Accurate and early diagnosis of this disease using OCT images is crucial for the prevention of blindness. In recent years, several deep learning methods have been very successful in automating the process of detecting retinal diseases from OCT images. However, most methods face reliability and interpretability issues. In this study, we propose a deep residual network for the classification of four classes of retinal diseases, namely diabetic macular edema (DME), choroidal neovascularization (CNV), DRUSEN and NORMAL in OCT images. The proposed model is based on the popular architecture called ResNet50, which eliminates the vanishing gradient problem and is pre-trained on large dataset such as ImageNet and trained end-to-end on the publicly available OCT image dataset. We removed the fully connected layer of ResNet50 and placed our new fully connected block on top to improve the classification accuracy and avoid overfitting in the proposed model. The proposed model was trained and evaluated using different performance metrics, including receiver operating characteristic (ROC) curve on a dataset of 84,452 OCT images with expert disease grading as DRUSEN, CNV, DME and NORMAL. The proposed model provides an improved overall classification accuracy of 99.48% with only 5 misclassifications out of 968 test samples and outperforms existing methods on the same dataset. The results show that the proposed model is well suited for the diagnosis of retinal diseases in ophthalmology clinics.

Construction and Multicenter Diagnostic Verification of Intelligent Recognition System for Endoscopic Images From Early Gastric Cancer Based on YOLO-V3 Algorithm

Introduction

Endoscopy is an important tool for the diagnosis of early gastric cancer. Therefore, a combination of artificial intelligence and endoscopy has the ability to increase the speed and efficiency of early gastric cancer diagnosis. YOU ONLY LOOK ONCE (YOLO) is an advanced object detection depth neural network algorithm that has not been widely used in gastrointestinal image recognition.

Objective

We developed an artificial intelligence system herein referred to as “EGC-YOLO” for the rapid and accurate diagnosis of endoscopic images from early gastric cancer.

Methods

More than 40000 gastroscopic images from 1653 patients in Yixing people’s Hospital were used as the training set for the system, while endoscopic images from the other two hospitals were used as external validation test sets. The sensitivity, specificity, positive predictive value, Youden index and ROC curve were analyzed to evaluate detection efficiencies for EGC-YOLO.

Results

EGC-YOLO was able to diagnose early gastric cancer in the two test sets with a high superiority and efficiency. The accuracy, sensitivity, specificity and positive predictive value for Test Sets 1 and 2 were 85.15% and 86.02%, 85.36% and 83.02%, 84.41% and 92.21%, and 95.22% and 95.65%, respectively. In Test Sets 1 and 2, the corresponding Threshold-values were 0.02, 0.16 and 0.17 at the maximum of the Youden index. An increase in Threshold-values was associated with a downward trend in sensitivity and accuracy, while specificity remained relatively stable at more than 80%.

Conclusions

The EGC-YOLO system is superior for the efficient, accurate and rapid detection of early gastric cancer lesions. For different data sets, it is important to select the appropriate threshold-value in advance to achieve the best performance of the EGC-YOLO system.

Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images

Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model.

Self-assessment and deep learning-based coronavirus detection and medical diagnosis systems for healthcare

Coronavirus is one of the serious threat and challenge for existing healthcare systems. Several prevention methods and precautions have been proposed by medical specialists to treat the virus and secure infected patients. Deep learning methods have been adopted for disease detection, especially for medical image classification. In this paper, we proposed a deep learning-based medical image classification for COVID-19 patients namely deep learning model for coronavirus (DLM-COVID-19). The proposed model improves the medical image classification and optimization for better disease diagnosis. This paper also proposes a mobile application for COVID-19 patient detection using a self-assessment test combined with medical expertise and diagnose and prevent the virus using the online system. The proposed deep learning model is evaluated with existing algorithms where it shows better performance in terms of sensitivity, specificity, and accuracy. Whereas the proposed application also helps to overcome the virus risk and spread.

A Comparative Analysis of Machine Learning Algorithms to Predict Alzheimer's Disease

Alzheimer's disease has been one of the major concerns recently. Around 45 million people are suffering from this disease. Alzheimer's is a degenerative brain disease with an unspecified cause and pathogenesis which primarily affects older people. The main cause of Alzheimer's disease is Dementia, which progressively damages the brain cells. People lost their thinking ability, reading ability, and many more from this disease. A machine learning system can reduce this problem by predicting the disease. The main aim is to recognize Dementia among various patients. This paper represents the result and analysis regarding detecting Dementia from various machine learning models. The Open Access Series of Imaging Studies (OASIS) dataset has been used for the development of the system. The dataset is small, but it has some significant values. The dataset has been analyzed and applied in several machine learning models. Support vector machine, logistic regression, decision tree, and random forest have been used for prediction. First, the system has been run without fine-tuning and then with fine-tuning. Comparing the results, it is found that the support vector machine provides the best results among the models. It has the best accuracy in detecting Dementia among numerous patients. The system is simple and can easily help people by detecting Dementia among them.