Brain tumor image identification and classification on the internet of medical things using deep learning

Abstract

The health services research network is showing a lot of interest in the Internet of Medical Things (IoMT). In IoMT, the Internet is used to help compile important health-related data. A brain tumor is caused by a mass of random cells inside the brain, which is dangerous and harmful to the brain. Today, it is difficult to accurately recognise brain images. In order to find and correctly categorize malignant cells in recognizing brain pictures, this research offers a support value-based deep neural network (SDNN) in e-Health care administration utilizing the IoMT innovation. As a starting point, a database of investigation is created using picture data based on IoT innovation and clinical images. The input brain picture is subjected to skull stripping during the preprocessing stage in order to isolate the desired brain area. The preprocessed output pictures are then used to extract the useful characteristics, such as entropy, geometric, and texture features. Finally, based on the collected characteristics, the proposed support value based adaptive deep neural network (SDNN) identification classifies the brain pictures as normal or abnormal. The results of the experiments are examined to show how the suggested recognition approach outperforms the ones already in use.

Keywords

• Skull stripping
• Geometric features
• Texture feature
• Entropy feature
• Classification