Brain Tumor Detection Analysis Using CNN: A Review

A Brain Tumor is essentially a malformed cell growth that can be cancerous and non-cancerous. The tumor in the Brain is the most dangerous disease and can be diagnosed easily and reliably with the help of detection of the tumor with automated techniques on MRI Images. Several methods of efficient diagnosis and segmentation of brain tumors have been suggested by many researchers for effective tumor detection. A review method involving two-stage approaches for 20 research papers published in the period from 2000 to 2020 has been conducted to learn about tumor detection in MRI images. There has also been a comparative study of various processes. Two key problems are discovered after an intensive analysis procedure, Image Restoration and Image Enhancement. Which are solved and clarified with suitable methodologies. The introduction of quantitative image analysis resulted in fields such as MRI Images. On-going area for interest in brain tumors, in particular focus on White Matter, CSF, GM. The novel method uses the CNN classification technique and has been used to disregard the dataset picture algorithm error. The implementation of the suggested model is applied in the Python and TensorFlow environment. Algorithms and methodologies used to solve specific research problems were included in the results and along with their strengths and limitations. This examines the quantitative characteristics of brain tumors, such as shape, texture, and signal intensity, to predict high accuracy with a low error rate and the potential for future work in the field.