[LGPR] Publication Success Series- Dr Malathi Marichamy

#LGPR #SCI #SCOPUS #PublicationSuccess

Dr. Malathi Marichamy, LGPR candidate, is the corresponding author and first author.

Unique Feature of the LGPR programme is that the Postdoctoral Candidate gets the opportunity to be both the first as well as co-corresponding author. I do not know many programmes that encourage this kind of an ethical practice. Academic world has been marred with unethical practices world-wide and it is pleasing to see such exemplary ethical policies enforced by the Lincoln University College Malaysia & LGPR's team. ______ Prof. Dr. Yong J Baek, Korean Association of Industries

Traitement du Signal, 42(4),1935-1943 (2025)

Journal Indexes: Impact factor 1

Indexing: SCI, SCIE & EI Compendex

H-Index: 31

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Traitement du Signal (TS) is a top-rated international journal committed to the dissemination of advances in the field of signal processing, imaging and visioning. Since its founding in 1984, the journal has published articles that present original research results of a fundamental, methodological or applied nature. Indexed in international databases, the TS provides a valuable reference to researchers, engineering, teachers and students across the globe. The editorial board welcomes articles on the latest and most promising results of academic research, including both theoretical results and case studies.

Focus and Scope

The TS welcomes original research papers, technical notes and review articles on various disciplines, including but not limited to:

• Signal processing

• Imaging

• Visioning

• Control

• Filtering

• Compression

• Data transmission

• Noise reduction

• Deconvolution

• Prediction

• Identification

• Classification

Publication Frequency

The TS is published regularly by the IIETA, with six regular issues (excluding special issues) and one volume per year.

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Enhanced Brain and Lung Tumor Detection by Explainable AI Techniques

https://doi.org/10.18280/ts.420408

Malathi Marichamy*

Lincoln University College, Petaling Jaya, Malaysia

https://orcid.org/0000-0002-1748-1280

Nagraj Pandian

Department of ECE, Vadapalani Campus, SRM Institute of Science and Technology, Chennai, India

https://orcid.org/0000-0002-0040-2515

Sujatha Kesavan

Department of Electrical& Electronics Engineering, Dr. M.G.R. Educational and Research Institute, Chennai- India

https://orcid.org/0000-0003-2417-0760

Mudassir Khan

Department of Computer Science, College of Computer Science, Applied College Tanumah, King Khalid University, Abha 61421, Saudi Arabia

Sai Kiran Oruganti

Faculty of Built Science & Engineering, Lincoln University College, Petaling Jaya 47810, Malaysia

https://orcid.org/0000-0003-4601-2907

Abstract

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Cancer accounts for most deaths worldwide, and cases of brain and lung tumors are emerging at a rapid pace. Early detection is of prime importance for better patient outcomes, but the conventional methods of diagnosing cancer rely upon MRI scans, and they are time-consuming, two-dimensional, and a potential source of inaccuracies. In India alone, more than 70,000 cases are reported of lung cancer. About 50,000 individuals have brain tumors. This research uses deep learning models-sequential model and the pre-trained VGG-16 model-to provide accurate classification for brain and lung tumors from MRI and CT images. With a combination of machine learning and image processing, the automated system reduces false negatives and false positives, thereby attaining high accuracy in diagnosis. Additionally, the use of Explainable AI (XAI) techniques improves the interpretation of predictions by healthcare professionals. These advanced, automated solutions are thus directed toward enhanced early cancer detection in the pursuit of better patient outcomes‎.

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