Md. Hasnain Ali

Greetings! I am Md. Hasnain Ali, an undergraduate student majoring in Computer Science and Engineering at the University of Rajshahi, Bangladesh. Currently, I am conducting research in Deep Learning and Computer Vision for Medical Image Processing.

Throughout my academic journey, I have gained invaluable insights into the world of Computer Science and the significance of classical strategies by applying them to impactful projects. My passion lies in leveraging technology to create tangible and efficient solutions for real-world problems.

I am passionate about finding new ways to benefit humanity through the power of AI, particularly in the field of Computer Vision. Additionally, I am exploring the sector of Natural Language Processing (NLP), Large Language Model (LLM), and Generative AI.

I specialize in Python programming and have experience with popular machine learning and deep learning tools such as TensorFlow, Keras, and Scikit-Learn. I am also knowledgeable in web frameworks like Django and Flask. In addition to Python, I am proficient in C, C++, Java, and JavaScript.

Beyond academics, I enjoy traveling, challenging myself through hikes, watching movies, and listening to music.

Research Interest

Medical Image Processing Computer Vision Natural Language Processing Signal Processing

Selected Publications

Competency Comparison of Deep Neural Networks for Identifying Gender in Color Fundus Photographs

Md. Ahanaf Arif Khan, Md. Hasnain Ali, Nirjor Saha, and 2 more authors

In 2023 26th International Conference on Computer and Information Technology (ICCIT) , Dec 2023

Abs Bib HTML

Retina specialists and researchers have been using color fundus photographs for almost 100 years for diagnosing retinal diseases, systemic disorders and recognizing humans. In 2018, researchers for the first time showed that gender identification can also be done by color fundus photographs using a deep neural network (DNN). Subsequent studies further confirmed the capability of DNNs in fundus photograph-based gender identification. However, the findings of these studies cannot be directly comparable with each other, since different datasets were used in different studies. In this paper, we conduct a competency comparison of six DNNs in gender identification by using a single dataset of color fundus photographs.
@inproceedings{Khan2023, author = {Khan, Md. Ahanaf Arif and Ali, Md. Hasnain and Saha, Nirjor and Shoumik, Md. Sadman Shakib and Biswas, Sangeeta}, title = {Competency Comparison of Deep Neural Networks for Identifying Gender in Color Fundus Photographs}, booktitle = {2023 26th International Conference on Computer and Information Technology (ICCIT)}, publisher = {IEEE}, doi = {10.1109/iccit60459.2023.10441623}, month = dec, year = {2023}, }