Abstract
Diagnosing brain diseases possess various inbuilt complexities to the nature of the diagnostic process. Brain tumor, Stroke, and Hemorrhage are the commonly prevailing disease and comprise more complexity in diagnosing where there arises the confusion in case of high grade or low-grade tumor and acute or sub-acute stroke. In general most of the prevailing algorithms is suited for the predicted of the image only employing the MRI or CT image. The paper mainly focused on the employment of a suitable proposed algorithm to adopt both the CT and MRI images for precise segmentation and classification. The segmentation algorithm is a map (map a posterior) based graph cut method The segmentation results are compared with the existing methods like (FCM) Fuzzy C Means and KFCM Kernel Fuzzy C Means and it is proved that our proposed system outperformed to the performance metrics. An improved VGG 16architecture is proposed for efficient classification. The overall classification results proved to be more efficient when compared with the existing R-CNN and NS-CNN methods. The paper focused on overcoming the difficulty and make a clear understanding of segmenting and classification irrespective of the nature of the diagnostic process.
Highlights
The brain diseases have become a predominant hassle amongst the human beings in which the brain tumor, stroke, and hemorrhage is in most common difficulty [1]
The paper focused on overcoming the difficulty and make a clear vicinity for segmenting via map-based graph cut method and classification via improved VGG 16 architecture
Every CNN-FCN framework were trained individually on the image data that are preprocessed by various settings of window width.By their integration, the study achieved efficient results on both segmentation and binary classification of hemorrhagic lesions when compared with all the CNN and FCN model.The study obtained net segmentation efficiency at 80% accuracy and 82% recall which is greater than nearly 3.5% compared with the single FCN model.[17]addressed the issue of BH identification in the earlier stage of haemorrhage.For solving the problem a CNN approach known as AlexNet integrated with SVM classifier were trained for the brain CT images into the corresponding non-hemorrhage and hemorrhage images
Summary
The brain diseases have become a predominant hassle amongst the human beings in which the brain tumor, stroke, and hemorrhage is in most common difficulty [1]. The treatment process greatly depends on the MRI and CT forms of diagnosis for assessing those tumors, stroke and hemorrhage. This diagnostic process restricts manual segmentation in a fully cheap time with accurate quantitative measurements. This paper segments and classifies the high grade and low-grade brain tumor image with BRATS 2015 dataset.[2]. The paper proposed a segmentation and classification technique for the detection of stoke lesions on the basis of DWI with ISLES dataset by Revised Manuscript Received on June 05, 2020. The paper focused on overcoming the difficulty and make a clear vicinity for segmenting via map-based graph cut method and classification via improved VGG 16 architecture
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