Deep Learning-Driven 3D CNN for Microscopic Brain Tumor Detection and Feature Optimization

Detection of brain tumor is one of the important healthcare aspects and deep learning techniques have been receiving vast attention in this area, and using the Kaggle dataset, the work achieved 99.8% accuracy classification of brain tumors. The classification was split into three types of brain tumors: glioma, meningioma, pituitary gland, and healthy brains. An example of deep learning models would be CNN. These have enabled the extraction of hierarchical features from complex visual data leading to powerful, highly accurate models. Another promising direction is transfer learning, where pretrained models are fine-tuned on new datasets or tasks in an effort to classify brain tumors. However, in brain tumor detection, there are challenges including the fact that the glioma and stroke tumors do not contrast well, thereby complicating the segmentation and classification processes. In addition, the detection of the tumor volume still remains a challenge, since it is possible for the tumor to be masked as a normal region. Its present machine learning techniques have limitations, which means the design of a lightweight model aimed to provide accuracy in very few computational times is well necessary. The fusion of multiple sequences with CNN models has indicated the potentiality for glioma detection. The merged sequence provides more information than a single sequence, and the proposed model was trained on the BRATS series for the detection of glioma. In a nutshell, deep learning methods had already made the best contributions toward the detection of brain tumors, but a generic technique, which can work through slight variations in training and testing images, is still required. Further research could be done for diagnosing the degree of brain tumors with a much more sensitive image acquisition by using real patient data through various scanners.