| dc.description.abstract |
Tumor indicates unfettered presence of a cluster of cells in a specific area of the body part. Brain
tumor is considered one of the most common tumors for both men and women and can lead to
high death risk if patients fail to obtain appropriate medical treatment. In order to diagnose brain
tumors, electronic modalities are integrated and among them MRI is a popular one. For MRI brain
tumor region analysis segmentation, detection and classification are considered as important steps
in digital imaging pathology laboratory. Existing state-of-the-art approaches demand widespread
amount of supervised training data from pathologists and may still accomplish poor results in
images from unseen tissue types. In this study, two approaches each for Conventional ML and
Deep ML have been presented to identify brain tumors from MRI images. For Conventional ML,
a suitable framework has been presented where for the first time in compare to all other existing
accessible approaches morphological operations has been incorporated to eliminate undesirable
regions and to assist segmentation and identification of region of interests. Compared with
existing state-of the-art supervised models, our method generalizes considerably improved
identified results on brain tumor cells deprived of training data. Even with training data, our
approach attains the identical performance without supervision cost. This study results indicates
an accuracy rate above 96.23% accuracy associated to existing works. For Deep ML, another
framework is presented where 97.84% accuracy is achieved. |
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