Towards ovarian cancer diagnostics: A vision transformer-based computer-aided diagnosis framework with enhanced interpretability DOI Creative Commons
Abdulrahman Alahmadi

Results in Engineering, Год журнала: 2024, Номер 23, С. 102651 - 102651

Опубликована: Авг. 2, 2024

Ovarian cancer, a significant threat to women's health, demands innovative diagnostic approaches. This paper introduces groundbreaking Computer-Aided Diagnosis (CAD) framework for the classification of ovarian integrating Vision Transformer (ViT) models and Local Interpretable Model-agnostic Explanations (LIME). ViT models, including ViT-Base-P16-224-In21K, ViT-Base-P16-224, ViT-Base-P32-384, ViT-Large-P32-384, exhibit exceptional accuracy, precision, recall, overall robust performance across diverse evaluation metrics. The incorporation stacked model further enhances performance. Experimental results, conducted on UBC-OCEAN training testing datasets, highlight proficiency in accurately classifying cancer subtypes based histopathological images. ViT-Large-P32-384 stands out as top performer, achieving 98.79% accuracy during 97.37% testing. Visualizations, Receiver Operating Characteristic (ROC) curves (LIME), provide insights into discriminative capabilities enhance interpretability. proposed CAD represents advancement diagnostics, offering promising avenue accurate transparent multi-class

Язык: Английский

Toward Inclusive Smart Cities: Sound-Based Vehicle Diagnostics, Emergency Signal Recognition, and Beyond DOI Creative Commons
Amr E. Eldin Rashed, Yousry AbdulAzeem, Tamer Ahmed Farrag

и другие.

Machines, Год журнала: 2025, Номер 13(4), С. 258 - 258

Опубликована: Март 21, 2025

Sound-based early fault detection for vehicles is a critical yet underexplored area, particularly within Intelligent Transportation Systems (ITSs) smart cities. Despite the clear necessity sound-based diagnostic systems, scarcity of specialized publicly available datasets presents major challenge. This study addresses this gap by contributing in multiple dimensions. Firstly, it emphasizes significance diagnostics real-time faults through analyzing sounds directly generated vehicles, such as engine or brake noises, and classification external emergency sounds, like sirens, relevant to vehicle safety. Secondly, paper introduces novel dataset encompassing environmental noises specifically curated address absence datasets. A comprehensive framework proposed, combining audio preprocessing, feature extraction (via Mel Spectrograms, MFCCs, Chromatograms), using 11 models. Evaluations both compact (52 features) expanded (126 representations show that several classes (e.g., Engine Misfire, Fuel Pump Cartridge Fault, Radiator Fan Failure) achieve near-perfect accuracy, though acoustically similar Universal Joint Failure, Knocking, Pre-ignition Problem remain challenging. Logistic Regression yielded highest accuracy 86.5% (DB1) features, while neural networks performed best DB2 DB3, achieving 88.4% 85.5%, respectively. In second scenario, Bayesian-Optimized Weighted Soft Voting with Feature Selection (BOWSVFS) approach significantly enhancing 91.04% DB1, 88.85% DB2, 86.85% DB3. These results highlight effectiveness proposed methods addressing key ITS limitations accessibility individuals disabilities auditory-based recognition systems.

Язык: Английский

Процитировано

0
Towards ovarian cancer diagnostics: A vision transformer-based computer-aided diagnosis framework with enhanced interpretability DOI Creative Commons
Abdulrahman Alahmadi

Results in Engineering, Год журнала: 2024, Номер 23, С. 102651 - 102651

Опубликована: Авг. 2, 2024

Ovarian cancer, a significant threat to women's health, demands innovative diagnostic approaches. This paper introduces groundbreaking Computer-Aided Diagnosis (CAD) framework for the classification of ovarian integrating Vision Transformer (ViT) models and Local Interpretable Model-agnostic Explanations (LIME). ViT models, including ViT-Base-P16-224-In21K, ViT-Base-P16-224, ViT-Base-P32-384, ViT-Large-P32-384, exhibit exceptional accuracy, precision, recall, overall robust performance across diverse evaluation metrics. The incorporation stacked model further enhances performance. Experimental results, conducted on UBC-OCEAN training testing datasets, highlight proficiency in accurately classifying cancer subtypes based histopathological images. ViT-Large-P32-384 stands out as top performer, achieving 98.79% accuracy during 97.37% testing. Visualizations, Receiver Operating Characteristic (ROC) curves (LIME), provide insights into discriminative capabilities enhance interpretability. proposed CAD represents advancement diagnostics, offering promising avenue accurate transparent multi-class

Язык: Английский

Процитировано

1