IoMT‐Based Multiple Disease Detection From Medical Images Using Vision Transformer‐Based Multi‐Scale Residual DenseNet With Hybrid Heuristic Strategies

In the Internet of Medical Things (IoMT), medical images play a vital role in healthcare systems by enabling accurate diagnosis, effective treatment planning, and continuous monitoring of various diseases. Deep learning models have strong potential to facilitate early detection by recognizing patterns in medical images. Although deep learning systems are more advanced, ambiguity continues to act as a major obstacle to progress. Ambiguity typically arises in areas such as accuracy, interpretability, and generalization. The challenge lies in balancing high performance with transparent and understandable decision‐making processes. This uncertainty can hinder trust computation and practical deployment, especially in critical applications. Moreover, these models face challenges with data handling and often lack robustness, which affects their reliability. To address the above‐mentioned issues, a novel multi‐disease detection framework is proposed using a deep learning approach. The input medical images are gathered from online datasets using the IoMT‐enabled devices. To detect different kinds of diseases, an adaptive vision transformer‐based multi‐scale residual dense network (AViT‐MRDNet) is developed. This model processes the input images to classify four types of diseases including skin lesions, brain tumors, lung tumors, and breast cancer. Here, the hybrid bonobo with rat swarm optimizer (HB‐RSO) is developed to optimize the parameters. Finally, the proposed framework produces the disease detection results. The overall performance of the implemented framework is verified by using various validation metrics such as precision, accuracy, false discovery rate, sensitivity, F 1‐score, false positive rate, specificity, Mathew correlation coefficient, false negative rate, and negative predictive value to showcase the effectiveness in detecting the multi‐disease. It attains an accuracy of 96.022, sensitivity of 95.164, specificity of 96.880, and precision of 96.825. Thus, these findings prove that the introduced framework has enhanced the system performance. The hybrid optimization algorithm in the proposed approach offers a robust solution for doctors and healthcare professionals to predict multiple diseases in patients. Therefore, it shows that the developed model is superior and delivers more reliable performance to other frameworks. The result confirmed that the proposed model can accurately and precisely detect the multi‐disease and help the patients for early diagnosis. It leverages the strength of the vision transformer and the residual densenet to enhance the system performance.