Stomach Abnormality Detection via Ensembling Architecture using Multimodal Inputs: Improved BIRCH-Based Image Segmentation

Stomach abnormalities such as ulcers, tumors, and inflammation often progress silently, remaining undetected until they reach advanced stages, which significantly reduce treatment effectiveness. Traditional diagnostic methods rely heavily on invasive procedures and often fail to detect early signs, especially when symptoms are minimal or absent. There is a critical need for non-invasive, accurate, and early detection techniques that can analyze diverse patient data effectively. Therefore, this research proposes an innovative ensemble architecture combining Bi-GRU, DBN, and RNN (En-BiDBRNN) models for stomach abnormality detection with an improved segmentation model. Each modality of input data (patient data) and iris images undergoes individual preprocessing steps. Subsequently, improved balanced iterative reducing and clustering using hierarchy-based segmentation is applied to the filtered images. Moreover, feature extraction and augmentation take place. Then, the features are processed independently using bidirectional gated recurrent unit, deep belief network, and recurrent neural network for stomach abnormality detection. Finally, the scores obtained from these models are combined using an enhanced score-level fusion method. Experimental results demonstrate that En-BiDBRNN significantly outperforms existing models across various training data sizes, achieving up to 96% accuracy at 90% training data. These findings validate the model's robustness, positioning En-BiDBRNN as a promising solution for early stomach abnormality detection.