For study of brain tumor detection and segmentation the MRI Images is very useful in recent years. Due to MRI Images we can detect the brain tumor. For detection of unusual growth of tissues and blocks of blood in nervous system can be seen in an MRI Images. The first step of detection of brain tumor is to check the symmetric and asymmetric Shape of brain which will define the abnormality. After this step the next step is segmentation which is based on two techniques 1) F-Transform (Fuzzy Transform) 2) Morphological operation. These two techniques are used to design the image in MRI. Now by this help of design we can detect the boundaries of brain tumor and calculate the actual area of tumor. In this the f-transform is used to give the certain information like rebuilt of missing edges and extracting the silent edges. Accuracy and clarity in an MRI Images is dependent on each other.
Skin malignant growth is the most widely recognized, everything being equal. Between 40 to 50 percent of all disease cases analyzed each year are skin malignant growth. Melanomas represent just four percent of all skin malignant growth cases yet are undeniably more perilous. Of all skin disease-related passing’s, 79 percent are from melanoma. Skin disease can be relieved if distinguished early. To appropriately distinguish melanoma, there is a need for a skin test. This is an obtrusive method and is the reason there is a requirement of a conclusion framework that can annihilate the skin test strategy emerges. We proposed to build up a Computer-Aided System that is equipped for ordering a skin injury as threatening or favorable by utilizing the ABCD rule which represents Asymmetry, Border, Color, Diameter of the skin sore. Further, the preprocessed pictures are portioned and commotions are taken out from the Dermoscopic pictures for instance hair and air bubbles. Also, finally, by utilizing a classifier, the proposed system identifies the pictures as favorable or harmful.
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