Abstract
Medical Image Analysis (MIA) is one of the active research areas in computer vision, where brain tumor detection is the most investigated domain among researchers due to its deadly nature. Brain tumor detection in magnetic resonance imaging (MRI) assists radiologists for better analysis about the exact size and location of the tumor. However, the existing systems may not efficiently classify the human brain tumors with significantly higher accuracies. In addition, smart and easily implementable approaches are unavailable in 2D and 3D medical images, which is the main problem in detecting the tumor. In this paper, we investigate various deep learning models for the detection and localization of the tumor in MRI. A novel two-tier framework is proposed where the first tire classifies normal and tumor MRI followed by tumor regions localization in the second tire. Furthermore, in this paper, we introduce a well-annotated dataset comprised of tumor and normal images. The experimental results demonstrate the effectiveness of the proposed framework by achieving 97% accuracy using GoogLeNet on the proposed dataset for classification and 83% for localization tasks after fine-tuning the pre-trained you only look once (YOLO) v3 model.
Highlights
Medical Image Analysis (MIA) has been one of the most interesting and active fields in the computer vision domain since the last decade
The results show that the accuracy of convolution neural network (CNN) is higher than support vector machine (SVM)
The second tier is based on the YOLOv3 model employed for brain tumor localization in magnetic resonance imaging (MRI)
Summary
Medical Image Analysis (MIA) has been one of the most interesting and active fields in the computer vision domain since the last decade. The main challenging aspect in human brain tumor classification is the lack of data that is not available publicly, such as the RIDER data set [8], 71 MR images [9], and BraTS 2013 [10]. Another key issue with the state-of-the-art methods for human brain tumors is the absence of efficient classifiers with significantly higher accuracies. A technique is innovated to detect a tumor in MRI based on accurate image processing and deep learning techniques.
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