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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: July 2026
Volume 11 | Issue 7
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Impact Factor : 8.14
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| Paper Title: | A Hybrid Quantum-Weighted Deep Feature and Texture-Based Framework for Leukemia Microscopic Image Classification |
| Authors Name: | Mayank Banchhor , Deepak Rao Khadatkar |
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| Author Reg. ID: |
IJRTI_213644
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| Published Paper Id: | IJRTI2606132 |
| Published In: | Volume 11 Issue 6, June-2026 |
| DOI: | |
| Abstract: | Leukemia is a life-threatening hematological malignancy characterized by the uncontrolled proliferation of abnormal white blood cells, where early and accurate diagnosis is crucial for effective treatment and improved patient survival. Traditional microscopic examination of blood smear images is labor-intensive, time-consuming, and highly dependent on expert interpretation, motivating the development of automated computer-aided diagnostic systems. This paper proposes a novel Quantum-Weighted Hybrid Feature Fusion Framework for automated leukemia cell classification using microscopic blood smear images. Initially, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to enhance image quality and improve cellular visibility. Subsequently, a comprehensive set of handcrafted features, including quantum-inspired statistical descriptors, color moments, Gray-Level Co-occurrence Matrix (GLCM) features, Local Binary Pattern (LBP) features, wavelet coefficients, and morphological characteristics, are extracted to capture diverse cellular properties. In parallel, deep semantic representations are obtained using a pretrained ResNet50 network. A novel quantum-weighting mechanism based on entropy and purity measures is introduced to enhance the discriminative capability of deep features. The weighted deep features and handcrafted descriptors are fused into a unified hybrid feature vector, which is further optimized using Principal Component Analysis (PCA) to reduce redundancy while preserving significant information. Finally, a Bagged Ensemble classifier is employed for robust leukemia classification under a five-fold cross-validation framework. Experimental evaluation on leukemia microscopic image datasets demonstrates that the proposed framework achieves an accuracy of 98.00%, sensitivity of 98.00%, specificity of 98.00%, precision of 98.00%, F1-score of 98.00%, and an AUC of 0.997, outperforming conventional machine learning and ensemble-based approaches. The results confirm that the integration of quantum-inspired weighting, hybrid feature fusion, and ensemble learning provides an effective and reliable solution for automated leukemia diagnosis. |
| Keywords: | Leukemia Detection, Quantum Features, Deep Learning, ResNet50, Ensemble Learning, PCA, Medical Image Analysis |
| Cite Article: | "A Hybrid Quantum-Weighted Deep Feature and Texture-Based Framework for Leukemia Microscopic Image Classification", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 6, page no.b202-b214, June-2026, Available :http://www.ijrti.org/papers/IJRTI2606132.pdf |
| Downloads: | 00051 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2606132
Registration ID:213644
Published In: Volume 11 Issue 6, June-2026
DOI (Digital Object Identifier):
Page No: b202-b214 Country: Raipur, Chhattisgarh, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2606132 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2606132 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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