Affiliations:
University of Balochistan, Quetta, Pakistan
Correspondence:
[*]
Corresponding author. Naseer-u-Din, Department of Computer Science & IT, University of Balochistan, Quetta, Pakistan. Tel: +92-332-7967211; E-mail: chairpersomcs@gmail.com, drabasit@um.uob.edu.pk.
Abstract: Researchers used visual methods rigorously to improve brain tumor detection in MRI or CT scans, yet there remains a challenge to improve the detection accuracy. Further, the rise of deep learning methods improved tumor detection accuracy up to the mark. But again, many times, we face the challenges of having a bigger dataset and better computing power to achieve an improved and accurate trained model for every object classification problem. In this paper, we propose a deep learning framework single shot multi-box detector (SSD)-based model to detect tumors in the MRI scans. The proposed SSD model is the faster algorithm to detect the tumor even with the ability to detect the smallest spot in the low-resolution MRI scans. We additionally used a lightweight neural network architecture MobileNet v2 with SSD for faster and accurate object classification. The experimental results showed 98% accuracy with the proposed method after training with the smallest dataset of 250 MRI scans. We used the Kaggle database for training and testing the proposed model.
Keywords: Convolutional neural network (CNN), tumor detection, MobileNet
model, segmentation, single shot detector (SSD), medical imaging
