Deep-Quantitative Medical Image Analysis Methods Applied on Brain Tumor Diagnosis

Solving medical image diagnosis and image analysis problems has long been thought of in many research types in the last decades. Mostly the interpretations of medical data are being made by a medical expert. In terms of image interpretation by a human expert, it is entirely limited due to its subjectivity, complexity, extensive variations across different interpreters, and fatigue. After the success of deep learning in other real-world applications, it provides exciting solutions with reasonable accuracy for medical imaging and is seen as a critical method for future applications in the health sector. This study proposed the deep learning approach based on soft-max activation function to obtain more reliable medical image diagnosis results properly. The proposed model evaluated a brain tumor (MRI) medical dataset, alongside an assessment in terms of accuracy, MSE, and RMSE, as a qualitative analysis of the learned features and practical commendations. This research should help improve the application and refinement of the evaluated approaches in the future.

Keywords:

deep learning , Medical Image Analysis , Machine Intelligence