MultiResFF‐Net: Multilevel Residual Block‐Based Lightweight Feature Fused Network With Attention for Gastrointestinal Disease Diagnosis

International Journal of Intelligent Systems, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

Accurate detection of gastrointestinal (GI) diseases is crucial due to their high prevalence. Screening often inefficient with existing methods, and the complexity medical images challenges single‐model approaches. Leveraging diverse model features can improve accuracy simplify detection. In this study, we introduce a novel deep learning tailored for diagnosis GI through analysis endoscopy images. This innovative model, named MultiResFF‐Net, employs multilevel residual block‐based feature fusion network. The key strategy involves integration from truncated DenseNet121 MobileNet architectures. not only optimizes model’s diagnostic performance but also strategically minimizes computational demands, making MultiResFF‐Net valuable tool efficient accurate disease in A pivotal component enhancing introduction Modified MultiRes‐Block (MMRes‐Block) Convolutional Block Attention Module (CBAM). MMRes‐Block, customized component, optimally handles fused at endpoint both models, fostering richer sets without escalating parameters. Simultaneously, CBAM ensures dynamic recalibration maps, emphasizing relevant channels spatial locations. dual incorporation significantly reduces overfitting, augments precision, refines extraction process. Extensive evaluations on three datasets—endoscopic images, GastroVision data, histopathological images—demonstrate exceptional 99.37%, 97.47%, 99.80%, respectively. Notably, achieves superior efficiency, requiring 2.22 MFLOPS 0.47 million parameters, outperforming state‐of‐the‐art models cost‐effectiveness. These results establish as robust practical

Язык: Английский

Synergizing vision transformer with ensemble of deep learning model for accurate kidney stone detection using CT imaging

Alexandria Engineering Journal, Год журнала: 2025, Номер 127, С. 357 - 373

Опубликована: Май 15, 2025

Язык: Английский

A Novel Lightweight Deep Learning Based Approaches for the Automatic Diagnosis of Gastrointestinal Disease using Image Processing and Knowledge Distillation Techniques

Computer Methods and Programs in Biomedicine, Год журнала: 2024, Номер 260, С. 108579 - 108579

Опубликована: Дек. 30, 2024

Язык: Английский