A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI

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 twotier 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 finetuning the pre-trained you only look once (YOLO) v3 model.

Automated summary

Medical Image Analysis (MIA) is an active research area in computer vision, with brain tumor detection being a major focus due to its severity. However, existing systems may not efficiently classify brain tumors with high accuracy, and there is a lack of smart and easily implementable approaches in 2D and 3D medical images. This paper proposes a novel two-tier framework for tumor detection and localization in MRI, and introduces a well-annotated dataset. Experimental results demonstrate the effectiveness of the proposed framework, achieving 97% accuracy for classification and 83% for localization.