[LGPR] Publication Success Series- Dr Sreedhar Kumar Seetharaman in Engineering Proceedings- MDPI

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Dr Sreedhar Kumar Seetharaman, LGPR Postdoctoral Candidate at LUC Malaysia has published in the Engineering Proceedings.

Engineering Proceedings 124(1) (2026)

Journal Indexes: Impact factor | Cite Score 1.2

Indexing: SCOPUS

H-Index: 17

Aims & Scope

Aims

Engineering Proceedings (ISSN 2673-4591) provides an advanced forum for studies on engineering that are presented at an academic conference. It publishes proceeding papers and conference reports on particular subjects. The aim of Engineering Proceedings is to help the conference organizers to publish cutting-edge, original, and peer-reviewed outputs resulting from the conference. All kinds of materials that result from the conference, including but not limited to presentations, posters, and videos, can be published along with their paper in the journal if accepted by the academic editor after peer review.

Scope

• Chemical engineering

• Civil engineering

• Electric and electronic engineering

• Mechanical engineering

• Materials engineering

• Bioengineering

• Environmental engineering

• Computer and software engineering

• Biomedical engineering

  
  

An Automated Medical Diagnosis System for Neoplasm Medical Image Classification Using Supervised and Unsupervised Techniques

Sreedhar Kumar Seetharaman

Lincoln Global Postdoctoral Research (LGPR) Program, Lincoln University College, Petaling Jaya 47301, Selangor Darul Ehsan, Malaysia

Department of Information Science and Engineering, Sir. M. Visvesvaraya Institute of Technology, Bengaluru 562157, Karnataka, India

Basant Kumar

Department of Mathematics and Computer Science, Modern College of Business and Science, Muscat 133, Oman

Manjunath Chikkanjinappa Rajanna

Department of Computer Science and Engineering, Global Academic Technology, Bengaluru 560098, Karnataka, India

Syed Thouheed Ahmed

School of Computer Science and Engineering, REVA University, Bengaluru 560064, Karnataka, India

https://doi.org/10.3390/engproc2026124049

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Abstract: In this research, an improved automated medical prediction system, namely, the Neoplasm Medical Image Classification System (NMICS), is proposed. The proposed NMICS aims to robotically identify whether the given test magnetic resonance image (MRI) belongs to the tumor group or the non-tumor group using machine learning techniques. The proposed NMICS is divided into two stages, namely, the Train Medical Image Model (TMIM) and the Medical Image Prediction Stage (MIPS), respectively. In the TMIM stage, the NMICS performs various distinct operations including improving input medical image data set quality and consistency through standard arithmetic operations; extracting specific features (edge) from every individual medical image in the input medical image set using the CNN method; and separating the feature vector set of the input medical image set into two distinct clusters, namely, tumor and non-tumor, respectively, using the unsupervised k-means clustering technique. In the MIPS stage, the proposed (NMIC) system performs the same types of operations, including preprocessing and feature extraction, on the test medical image samples. Next, the NMICS maps and classifies the feature vector of the test medical image sample against trained medical image data set clusters using a KNN classifier. The investigation results show that the NMICS is well-suited to diagnosing whether the given medical image is grouped into the neoplasm category or the non-neoplasm group.