Machine Learning Algorithms for Classifying Disorder in Brain and Lung Biomedical Images

Biomedical imaging and its research guide tremendous impact on the medical field. Different types of biomedical imaging help doctors to diagnose and detect diseases in advance. The early diagnosis increases the life span of humans. The machine learning algorithms are incorporated with the biomedical images to make the Computer-Aided Diagnosis (CAD) system. The CAD will reduce the human efforts and the false negatives of the diagnosis. Any damage that happens to the cells is known as abnormality. The abnormalities can be found with the help of computer-aided systems. Brain tumor classification is a complex problem due to the size, shape, and intensity variation, the dataset consists of glioma, meningioma, and pituitary tumor images. The features from the deep architecture and wavelet scattering methods are explored for brain tumor classification. These features are classified by using a Support Vector Machine with a cubic kernel. The proposed method shows a promising accuracy while considering the state-of-the-art methods