DiagPCNN: Enhancing CNN through Pretrained Model for Brain Tumor Diagnosis

Analysis of medical images plays a vital role in the early detection and prognosis of diseases. Among various medical imaging, Magnetic Resonance Imaging(MRI) is valuable in providing insights into the brain artefact. Early classification of brain tumors has become indispensable for doctors and medical persons. With the recent advancements in deep learning, Convolution Neural Networks have gained widespread in the domain of disease diagnosis and medical imaging, due to their notable efficacy and high performance. In this study, we have proposed the DiagPCNN model which incorporates a pre-trained model with CNN for an increment in optimal efficacy of the manifest model. We have also analysed the pros and cons of the DiagPCNN model over eighteen different architectures and three DiagPCNN models outperforms the rest of models with superior accuracy of 96.63, 97.28 and 97.79% respectively. Hence, simplifying their skilfulness in detecting brain tumors to induce the manifest model of a productive and virtuous approach for enhanced prognosis.