A brain tumor occurs in humans when a normal cell turns into an aberrant cell inside the brain. Primarily, there are two types of brain tumors in Homo sapiens: benign tumors and malignant tumors. In brain tumor diagnosis, magnetic resonance imaging (MRI) plays a vital role that requires high precision and accuracy for diagnosis, otherwise, a minor error can result in severe consequences. In this study, we implemented various configured convolutional neural network (CNN) paradigms on brain tumor MRI scans that depict whether a person is a brain tumor patient or not. This paper emphasizes objective function values (OFV) achieved by various CNN paradigms with the least validation cross-entropy loss (LVCEL), maximum validation accuracy (MVA), and training time (TT) in seconds, which can be used as a feasible tool for clinicians and the medical community to recognize tumor patients precisely. Experimentation and evaluation were based on a total of 2189 brain MRI scans, and the best architecture shows the highest accuracy of 0.8275, maximum objective function value of 1.84, and an area under the ROC (AUC-ROC) curve of 0.737 to accurately recognize and classify whether or not a person has a brain tumor.

Pneumothorax is a critical medical condition among Human-beings. A severe Pneumothorax causes Collapsed Lungs. It is a life-threatening disease. Therefore, Pneumothorax detection is an important step for the prevention and curing of a patient. Pneumothorax can be classified into three major categories: Primary, Secondary, and Injury. Magnetic Resonance Imaging (MRI)-based Digital Imaging and Communications in Medicine (DICOM) files of Chest X-Ray (CXR) images provide insight and help the doctor to make an appropriate decision. An early decision can prevent the mortality rate among patients. Since the outbreak of the COVID-19 pandemic, the medical systems and staff have gone under massive pressure. Classification from a CXR image by an expert requires huge manpower and a longer time to determine. Deep learning-based automatic classification of Pneumothorax (CXR) images can assist the medical community in a fast diagnosis and reduce the burden of work overload. Doctors can focus on better treatment and cure of Pneumothorax. In this paper, we have proposed seven scratch Convolutional Neural Networks (CNN) architectures and compared them with another seven Transfer Learning models. The best-performing CNN model (PneuNetV1) is determined based on various standard performance metrics. It has gained the highest test accuracy, efficacy ratio, and F1-score of 0.9123, 5.2370, and 0.9220, respectively with a minimum training time. The obtained results are achieved through rigorous experimentation and yet provide satisfactory performance.