Enhanced Skin Disease Classification via Dataset Refinement and Attention-Based Vision Approach

Bioengineering, Journal Year: 2025, Volume and Issue: 12(3), P. 275 - 275

Published: March 11, 2025

Skin diseases are listed among the most frequently encountered diseases. such as eczema, melanoma, and others necessitate early diagnosis to avoid further complications. This study aims enhance of skin disease by utilizing advanced image processing techniques an attention-based vision approach support dermatologists in solving classification problems. Initially, is being passed through various steps quality dataset. These adaptive histogram equalization, binary cross-entropy with implicit averaging, gamma correction, contrast stretching. Afterwards, enhanced images for performing which based on encoder part transformers multi-head attention. Extensive experimentation performed collect results two publicly available datasets show robustness proposed approach. The evaluation shows competitive compared a state-of-the-art

Language: Английский

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A novel network-level fused deep learning architecture with shallow neural network classifier for gastrointestinal cancer classification from wireless capsule endoscopy images

BMC Medical Informatics and Decision Making, Journal Year: 2025, Volume and Issue: 25(1)

Published: March 31, 2025

Deep learning has significantly contributed to medical imaging and computer-aided diagnosis (CAD), providing accurate disease classification diagnosis. However, challenges such as inter- intra-class similarities, class imbalance, computational inefficiencies due numerous hyperparameters persist. This study aims address these by presenting a novel deep-learning framework for classifying localizing gastrointestinal (GI) diseases from wireless capsule endoscopy (WCE) images. The proposed begins with dataset augmentation enhance training robustness. Two architectures, Sparse Convolutional DenseNet201 Self-Attention (SC-DSAN) CNN-GRU, are fused at the network level using depth concatenation layer, avoiding costs of feature-level fusion. Bayesian Optimization (BO) is employed dynamic hyperparameter tuning, an Entropy-controlled Marine Predators Algorithm (EMPA) selects optimal features. These features classified Shallow Wide Neural Network (SWNN) traditional classifiers. Experimental evaluations on Kvasir-V1 Kvasir-V2 datasets demonstrate superior performance, achieving accuracies 99.60% 95.10%, respectively. offers improved accuracy, precision, efficiency compared state-of-the-art models. addresses key in GI diagnosis, demonstrating its potential efficient clinical applications. Future work will explore adaptability additional optimize complexity broader deployment.

Language: Английский

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Deep ensemble learning for gastrointestinal diagnosis using endoscopic image classification

PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2809 - e2809

Published: April 22, 2025

Transfer learning is a valuable tool for the effective assistance of gastroenterologists in powerful diagnosis medical images with fast convergence. It also intends to minimize time and estimated effort required improved gastrointestinal tract (GIT) diagnosis. GIT abnormalities are widely known be fatal disorders leading significant mortalities. includes both upper lower disorders. The challenges addressing issues complex need study. Multiple exist regarding computer-aided (CAD) endoscopy including lack annotated images, dark backgrounds, less contrast, noisy irregular patterns. Deep transfer have assisted various ways. goal proposed framework classification endoscopic enhanced accuracy. research aims formulate learning-based deep ensemble model, accurately classifying therapeutic purposes. model based on weighted voting two state-of-the-art (STA) base models, NasNet-Mobile EfficientNet. extraction regions interest, specifically sick portions, been performed using captured from procedure. Performance evaluation cross-dataset evaluation. datasets utilized include training dataset HyperKvasir test datasets, Kvasir v1 v2. However, alone cannot create robust due unequal distribution across categories, making promising approach development. has conducted by utilizing accuracy, precision, recall, Area under curve (AUC) score F1 performance metrics. work outperforms much existing models giving 97.83% 98.45% accuracy

Language: Английский

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Enhancing Ulcerative Colitis Diagnosis: A Multi-Level Classification Approach with Deep Learning

Computer Modeling in Engineering & Sciences, Journal Year: 2024, Volume and Issue: 140(1), P. 1129 - 1142

Published: Jan. 1, 2024

The evaluation of disease severity through endoscopy is pivotal in managing patients with ulcerative colitis, a condition significant clinical implications.However, endoscopic assessment susceptible to inherent variations, both within and between observers, compromising the reliability individual evaluations.This study addresses this challenge by harnessing deep learning develop robust model capable discerning discrete levels severity.To initiate endeavor, multi-faceted approach embarked upon.The dataset meticulously preprocessed, enhancing quality discriminative features images contrast limited adaptive histogram equalization (CLAHE).A diverse array data augmentation techniques, encompassing various geometric transformations, leveraged fortify dataset's diversity facilitate effective feature extraction.A fundamental aspect involves strategic incorporation transfer principles, modified ResNet-50 architecture.This augmentation, informed domain expertise, contributed significantly model's classification performance.The outcome research endeavor yielded highly promising model, demonstrating an accuracy rate 86.85%, coupled recall 82.11% precision 89.23%.

Language: Английский

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Vision Transformer Model for Gastrointestinal Tract Diseases Classification from WCE Images

Published: April 21, 2024

Accurate disease classification utilizing endoscopic images indeed poses a significant challenge within the field of gastroenterology. This research introduces methodology for assisting medical diagnostic procedures and detecting gastrointestinal (GI) tract diseases by categorizing features extracted from using Vision Transformer (ViT) models. We propose three ViT-inspired models classifying GI colon acquired through wireless capsule endoscopy (WCE). The highest achieved accuracy among our is 97.83%. conducted comparative analysis with pre-trained CNN (Convolutional Neural Network) namely, Xception, DenseNet121, MobileNet, alongside recent papers to validate findings.

Language: Английский

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Explainable AI for gastrointestinal disease diagnosis in telesurgery Healthcare 4.0

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 118, P. 109414 - 109414

Published: July 8, 2024

Language: Английский

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Applications of Artificial Intelligence and Machine Learning in Spine MRI

Bioengineering, Journal Year: 2024, Volume and Issue: 11(9), P. 894 - 894

Published: Sept. 5, 2024

Diagnostic imaging, particularly MRI, plays a key role in the evaluation of many spine pathologies. Recent progress artificial intelligence and its subset, machine learning, has led to applications within which we sought examine this review. A literature search major databases (PubMed, MEDLINE, Web Science, ClinicalTrials.gov) was conducted according Preferred Reporting Items for Systematic Reviews Meta-Analyses (PRISMA) guidelines. The yielded 1226 results, 50 studies were selected inclusion. Key data from these extracted. Studies categorized thematically into following: Image Acquisition Processing, Segmentation, Diagnosis Treatment Planning, Patient Selection Prognostication. Gaps proposed areas future research are discussed. Current demonstrates ability improve various aspects field, image acquisition analysis clinical care. We also acknowledge limitations current technology. Future work will require collaborative efforts order fully exploit new technologies while addressing practical challenges generalizability implementation. In particular, use foundation models large-language MRI is promising area, warranting further research. assessing model performance real-world settings help uncover unintended consequences maximize benefits patient

Language: Английский

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Analysis of Colorectal and Gastric Cancer Classification: A Mathematical Insight Utilizing Traditional Machine Learning Classifiers

Mathematics, Journal Year: 2023, Volume and Issue: 11(24), P. 4937 - 4937

Published: Dec. 12, 2023

Cancer remains a formidable global health challenge, claiming millions of lives annually. Timely and accurate cancer diagnosis is imperative. While numerous reviews have explored classification using machine learning deep techniques, scant literature focuses on traditional ML methods. In this manuscript, we undertake comprehensive review colorectal gastric detection specifically employing classifiers. This emphasizes the mathematical underpinnings detection, encompassing preprocessing feature extraction, classifiers, performance assessment metrics. We provide formulations for these key components. Our analysis limited to peer-reviewed articles published between 2017 2023, exclusively considering medical imaging datasets. Benchmark publicly available datasets cancers are presented. synthesizes findings from 20 16 cancer, culminating in total 36 research articles. A significant focus placed commonly used features, Crucially, introduce our optimized methodology both cancers. metrics reveals remarkable results: 100% accuracy types, but with lowest sensitivity recorded at 43.1% cancer.

Language: Английский

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Advancements in traditional machine learning techniques for detection and diagnosis of fatal cancer types: Comprehensive review of biomedical imaging datasets

Measurement, Journal Year: 2023, Volume and Issue: 225, P. 114059 - 114059

Published: Dec. 22, 2023

Language: Английский

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Emerging Trends and Advances in the Diagnosis of Gastrointestinal Diseases

BioScientific Review, Journal Year: 2023, Volume and Issue: 5(2), P. 118 - 143

Published: Sept. 8, 2023

Recently, Artificial Intelligence (AI)-based techniques, namely machine learning (ML) and deep (DL) have gained exceptional devotion in conducting the analysis of medical images because their capacity to provide outstanding results that can compete with specialists. Despite rise artificial intelligence-based research on peptic ulcer diseases, limited reviews are available concerning this area. For purpose, researcher reviewed intelligence techniques used for detecting classifying gastrointestinal diseases wireless capsule endoscopy images. Furthermore, study investigates tremendous potential disease has been cited prior literature. The findings demonstrated value WCE picture using techniques. Additionally, further, limitations were found availability datasets assessment measures, which an impact reproducibility experiments.

Language: Английский

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