Optimal Feature Selection-Based Medical Image Classification Using Deep Learning Model in Internet of Medical Things

Internet of Medical Things (IoMT) is the collection of medical devices and related applications which link the healthcare IT systems through online computer networks. In the field of diagnosis, medical image classification plays an important role in prediction and early diagnosis of critical diseases. 

Medical images form an indispensable part of a patient's health record which can be applied to control, handle and treat the diseases. But, classification of images is a challenging task in computer-based diagnostics. In this research article, we have introduced an improved classifier i.e., Optimal Deep Learning (DL) for classification of lung cancer, brain image, and Alzheimer's disease.

 The main goal of the paper is to derive an optimal feature selection model for effective medical image classification. To enhance the performance of the DL classifier, here Multi-texture, grey level features were selected for the analysis. The proposed results were implemented in MATLAB and compared with existing feature selection models and other classification approaches.

Keywords: IoMT, classification, deep learning, medical image, features, Crow search algorithm, optimization.