New Health of Things Approach to Classification and Detection of Brain Tumors Using Transfer Learning for Segmentation in IMR Images.

A multitude of diseases can afflict the human body, each potentially causing a range of health problems. Among the most devastating are brain diseases that originate from tumors, affecting the well-being of countless people worldwide. Brain tumors are a pathology that results in numerous sequelae, significantly impacting public spending due to the costs of hospital clinical treatments, exams, and medicines for patients. These factors are linked to the problem, resulting in significant financial impacts on the health sector worldwide. The problem of detection and segmentation in medical images provides new solutions through different methods based on computer vision. This study proposes a fully automatic model based on the Internet of Things (IoT), capable of classifying, detecting, and segmenting magnetic resonance images of brain tumors. The proposed model can classify MR images, detect the tumor region, and segment it through deep extractors and classifiers, combined with deep learning using the Detectron2 network for brain tumor detection and fine-tuning for brain tumor segmentation. The model was trained and tested with the dataset (Brain MRI Segmentation - LGG Segmentation Dataset), obtaining excellent classification results using a transfer learning model of DenseNet201 + SVM RBF, achieving 93.10% accuracy. For the Detectron2 network, 99.38% accuracy was achieved, with 99.41% for brain tumor segmentation. The fully automatic IoT-based model (Health of Things) efficiently classified, detected, and segmented brain tumors in MR images, surpassing different reputable works in the literature.