(untitled) (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Cancer is the large important origin of casualty in today world. Among many cancers, brain cancer has been becomes one of the lowest living rate. It is formed based on the brain tumor. However brain tumors are able to have diverse categories based on their shape, texture, and position. Appropriate identification and retrieval of the tumor types create potential the doctor to compose the right cure option and assist keep the patient’s life. Image processing has gained wide attention in medical analysis and health in recently. In general image processing methods, brain Magnetic Resonance Imaging (MRI) image collections cannot be processed efficiently on one computer due to large collection sizes and high computational costs. Hence, parallel computing and distributed system has been performed increasingly for brain MRI images in recently. In this paper, a novel Medical Image Cloud Processing (MICP) based distributed processing framework is proposed for brain MRI images by lesser computational time. In this work, image preprocessing is done by using the Adaptive Median Filtering (AMF) and image enhancement by Histogram Equalization (HE). The proposed MICP framework includes of Static Medical Image Cloud Processing (SMICP) and Dynamic Medical Image Cloud Processing (DMICP). In MICP framework, SMICP consists of two methods called Pure-Image and Big-Image. These methods are integrated to Genetic Algorithm based Fuzzy Local Information C-Means-MapReduce (GAFLICM-MR) algorithm to attain more optimized design and higher effectiveness. The core design of GAFLICM-MR framework is to make use of the rich computing resources given by means of the distributed system consequently as to apply efficient parallel processing. GAFLICM algorithm is also used as brain tumor segmentation in MRI images. In MICP framework, DMICP is developed via a parallel processing process of the distributed system. For retrieval and detection of brain Magnetic MRIimages into normal and tumor, Hybrid Kernel Convolution Neural Network (HKCNN) is developed in this work. Finally the results of the HKCNN classifier are compared to other previous works like precision, recall, f-measure, accuracy, time and memory.