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👋 Welcome to my research page!
👋 Welcome to my research page!
This is where I share my academic and research journey, focusing on machine learning, medical image analysis, and AI-driven solutions. Here, you’ll find details about my projects, publications, and methodologies, all aimed at advancing the field and solving real-world problems. Explore my work below, and feel free to reach out if you’d like to discuss ideas or collaborations! 📚✨
Publications
Publications
U-Trans: An Automated Swin-Transformer based model for Skin Lesion Segmentation
Author: A.L. Sharma, K. Sharma, U. P. Srivastava, P. Ghosal
Status: Submitted
Summary: This paper introduces U-Trans, a novel deep learning model leveraging Swin Transformers for enhanced skin lesion segmentation. The model is designed to improve accuracy and efficiency in automated skin cancer detection.
Conference Papers
A Transfer Learning based GUI for Skin Cancer Diagnosis and Classification using Dermoscopic Images
Author: U. P. Srivastava, K. M. Shedge, T. K. Koirala, P. Ghosal
Year: 2023
Conference: IEEE Silchar Subsection Conference (SILCON), Silchar, India
Pages: 1-8
DOI: 10.1109/SILCON59133.2023.10404940
Summary: This paper discusses a user-friendly graphical interface for diagnosing and classifying skin cancer using dermoscopic images, employing transfer learning techniques for enhanced accuracy and real-time results. Worked with a large imbalanced dataset.
A Comparative Study of Deep Learning Algorithms in Classifying Brain Cancer
Author: U. P. Srivastava
Year: 2023
Conference: 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India
Pages: 1-6
DOI: 10.1109/ICCCNT56998.2023.10306832
Summary: This study compares the performance of various deep learning algorithms in classifying brain cancer, analyzing the strengths and limitations of each model for medical applications.
Performance Analysis of an ANN-based Model for Breast Cancer Classification using Wisconsin Dataset
Author: U. P. Srivastava, Vaidehi V., T. K. Koirala, P. Ghosal
Year: 2023
Conference: International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC)
Pages: 1-5
DOI: 10.1109/ISACC56298.2023.10083642
Summary: This paper evaluates an Artificial Neural Network (ANN)-based model for breast cancer classification, focusing on the use of the Wisconsin dataset to identify the most accurate prediction model.
Book Chapters
AUTCD-Net: An Automated Framework for Efficient Covid-19 Diagnosis on Computed Tomography Scans
Author: P. Ghosal, A. Kumar, S. S. Kundu, U. P. Srivastava, A. Datta, H.K. Deva Sarma
Year: 2022
Book Title: Machine Learning in Information and Communication Technology
Publisher: Springer, Singapore
DOI: 10.1007/978-981-19-5090-2_10
Summary: This chapter presents AUTCD-Net, an automated framework leveraging machine learning to diagnose Covid-19 using CT scans. The method enhances diagnostic accuracy and efficiency during the pandemic.
Reviewer Contributions
Reviewer Contributions
As a reviewer for ITE Transactions on Media Technology and Applications in 2024, I had the opportunity to evaluate research papers in the field of media technology. This role allowed me to engage with cutting-edge work in the area, providing constructive feedback on topics such as medical media processing and technology applications, while ensuring high academic standards were upheld.
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