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

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