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S. Islam

3 papers hereh-index 591 citations9 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1
  • last author1

Across the 2 of 3 papers where every author was matched, so the position is known.

fields
  • cs.AI1
  • cs.CV1
  • eess.IV1
same name
  • S. Islam — 10 papers, h 17
  • S. Islam — 8 papers, h 27
  • S. Islam — 5 papers, h 18
  • S. Islam — 4 papers, h 2
  • S. Islam — 3 papers, h 8
  • S. Islam — 3 papers, h 7

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedAdaptive Local Binary Pattern: A Novel Feature Descriptor for Enhanced Analysis of Kidney Abnormalities in CT Scan Images using ensemble based Machine Learning Approach

2 citations · 2 across the 3 of their papers we have counts for

collaborators

3 papers

eess.IV2025

A Modified VGG19-Based Framework for Accurate and Interpretable Real-Time Bone Fracture Detection

Md. Ehsanul Haque, Abrar Fahim, Shamik Dey +4

Early and accurate detection of the bone fracture is paramount to initiating treatment as early as possible and avoiding any delay in patient treatment and outcomes. Interpretation…

cs.AI2025

Improving Chronic Kidney Disease Detection Efficiency: Fine Tuned CatBoost and Nature-Inspired Algorithms with Explainable AI

Md. Ehsanul Haque, S. M. Jahidul Islam, Jeba Maliha +3

Chronic Kidney Disease (CKD) is a major global health issue which is affecting million people around the world and with increasing rate of mortality. Mitigation of progression of C…

cs.CV2024★ 2 cited

Adaptive Local Binary Pattern: A Novel Feature Descriptor for Enhanced Analysis of Kidney Abnormalities in CT Scan Images using ensemble based Machine Learning Approach

Tahmim Hossain, Faisal Sayed, Solehin Islam

The shortage of nephrologists and the growing public health concern over renal failure have spurred the demand for AI systems capable of autonomously detecting kidney abnormalities…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.