Md. Easin Arafat

Work place: Institute of Information Technology, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh



Research Interests: Health Informatics, Machine Learning, Deep Learning


Md. Easin Arafat received the B.Sc. (Engineering) degree in Computer Science and Engineering from United International University, Bangladesh, in 2019 and holds a Master’s degree in Information Technology from the Institute of Information Technology, Jahangirnagar University, Bangladesh in 2022. He jointly organized the Data Science for Bioinformatics workshop with the UIU APPF in 2019 and the Trainer of Machine Learning and Its Applications short course with the Pi Research Consultancy Center in 2021. He served as a Research Assistant in Bioinformatics Research Laboratory from 2019 to 2020 at UIU. He has published many research articles in leading journals and prestigious international conferences including IEEE ACCESS, GENES, Computers in Biology and Medicine, Neural Computing and Application, ICECTE-2019, and TENSYMP-2020. He also served as a reviewer for a variety of journals in these disciplines. His main research interests are in the field of Machine Learning, Computational Biology, Bioinformatics, Health Informatics, and Deep Learning.

Author Articles
Enhancing Brain Tumor Classification in MRI: Leveraging Deep Convolutional Neural Networks for Improved Accuracy

By Shourove Sutradhar Dip Md. Habibur Rahman Nazrul Islam Md. Easin Arafat Pulak Kanti Bhowmick Mohammad Abu Yousuf

DOI:, Pub. Date: 8 Jun. 2024

Brain tumors are among the deadliest forms of cancer, and there is a significant death rate in patients. Identifying and classifying brain tumors are critical steps in understanding their functioning. The best way to treat a brain tumor depends on its type, size, and location. In the modern era, Radiologists utilize Brain tumor locations that can be determined using magnetic resonance imaging (MRI). However, manual tests and MRI examinations are time-consuming and require skills. In addition, misdiagnosis of tumors can lead to inappropriate medical therapy, which could reduce their chances of living. As technology advances in Deep Learning (DL), Computer Assisted Diagnosis (CAD) as well as Machine Learning (ML) technique has been developed to aid in the detection of brain tumors, radiologists can now more accurately identify brain tumors. This paper proposes an MRI image classification using a VGG16 model to make a deep convolutional neural network (DCNN) architecture. The proposed model was evaluated with two sets of brain MRI data from Kaggle. Considering both datasets during the training at Google Colab, the proposed method achieved significant performance with a maximum overall accuracy of 96.67% and 97.67%, respectively. The proposed model was reported to have worked well during the training period and been highly accurate. The proposed model's performance criteria go beyond existing techniques.

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