Fuzzy-Based Medical Image Processing and Analysis


  • Parul Saxena Convener & Head, Department of Computer Science, Soban Singh Jeena University, Campus Almora,263601, Uttarakhand, India
  • Vinay Saxena Professor, Department of Mathematics, Kisan Post Graduate College, Bahraich, 271801, Uttar Pradesh, India
  • Mule Shrishail Basvant Associate Professor, Department of Electronics & Telecommunication Engineering, Sinhgad College of Engineering, Pune-41
  • Yogesh Lohumi Assistant Professor, Computer Science, Graphic Era (Deemed to be University), Dehradun, India
  • Manish Saraswat Lloyd Institute of Engineering & Technology, Greater Noida
  • Akhil Sankhyan Lloyd Law College, Greater Noida
  • A. Deepak Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamilnadu
  • Anurag Shrivastava Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu


Fuzzy Logic, Medical Image Processing, Image Enhancement, Fuzzy C-Means Clustering, Diagnostic Decision Support


This study presents a thorough technique for fuzzy-based Medical picture handling and examination that means managing the innate vulnerabilities of medical pictures. The proposed strategy includes diagnostic decision support, image enhancement, segmentation, feature extraction, and fuzzy principles. fuzzy C-Means bunching and fuzzy choice trees become the overwhelming focus, further developing picture quality, exact division, and solid analytic outcomes. The procedure and potential have been approved through a thorough quantitative assessment of various datasets and imaging strategies. Remarkable commitments incorporate superior element portrayal, versatility to medical varieties, and easy-to-understand execution. Moral contemplations that accentuate patient protection and information security are foremost. This study lays the preparation for future examination and supports the combination of profound learning with fuzzy rationale, and continuous approval in genuine medical settings.


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N. Thakur, N. U. Khan and S. D. Sharma, "A Two Phase Ultrasound Image De-speckling Framework by Nonlocal Means on Anisotropic Diffused Image Data," Informatica, vol. 47, (2), pp. 221-234, 2023. Available: https://www.proquest.com/scholarly-journals/two-phase-ultrasound-image-de-speckling-framework/docview/2867614448/se-2. DOI: https://doi.org/10.31449/inf.v47i2.4378.

R. Thamizhamuthu and P. M. Subramanian, "Deep Learning-Based Dermoscopic Image Classification System for Robust Skin Lesion Analysis," Traitement Du Signal, vol. 40, (3), pp. 1145-1152, 2023. Available: https://www.proquest.com/scholarly-journals/deep-learning-based-dermoscopic-image/docview/2831412266/se-2. DOI: https://doi.org/10.18280/ts.400330.

A. Jawdekar and M. Dixit, "Deep Learning and Fuzzy Logic Based Intelligent Technique for the Image Enhancement and Edge Detection Framework," Traitement Du Signal, vol. 40, (1), pp. 351-359, 2023. Available: https://www.proquest.com/scholarly-journals/deep-learning-fuzzy-logic-based-intelligent/docview/2807002690/se-2. DOI: https://doi.org/10.18280/ts.400135.

Sitanaboina S.L. Parvathi, S. C. Bolem and J. Harikiran, "Depth Invariant 3D-CU-Net Model with Completely Connected Dense Skip Networks for MRI Kidney Tumor Segmentation," Traitement Du Signal, vol. 40, (1), pp. 217-225, 2023. Available: https://www.proquest.com/scholarly-journals/depth-invariant-3d-cu-net-model-with-completely/docview/2807002669/se-2. DOI: https://doi.org/10.18280/ts.400120.

P. K. Pachala and P. Bojja, "Prediction of Lungs Cancer in Medical Images Using Deep Learning Approach," Ingenierie Des Systemes d'Information, vol. 28, (1), pp. 217-223, 2023. Available: https://www.proquest.com/scholarly-journals/prediction-lungs-cancer-medical-images-using-deep/docview/2803914745/se-2. DOI: https://doi.org/10.18280/isi.280125.

B. Cardone, F. D. Martino and V. Miraglia, "A Novel Fuzzy-Based Remote Sensing Image Segmentation Method," Sensors, vol. 23, (24), pp. 9641, 2023. Available: https://www.proquest.com/scholarly-journals/novel-fuzzy-based-remote-sensing-image/docview/2904929818/se-2. DOI: https://doi.org/10.3390/s23249641.

S. K. Sarwar, M. Khan and Y. Alharbi, "Fast Local Laplacian Filter Based on Modified Laplacian through Bilateral Filter for Coronary Angiography Medical Imaging Enhancement," Algorithms, vol. 16, (12), pp. 531, 2023. Available: https://www.proquest.com/scholarly-journals/fast-local-laplacian-filter-based-on-modified/docview/2904635589/se-2. DOI: https://doi.org/10.3390/a16120531.

M. Eftekharian, A. Nodehi and R. Enayatifar, "ML-DSTnet: A Novel Hybrid Model for Breast Cancer Diagnosis Improvement Based on Image Processing Using Machine Learning and Dempster–Shafer Theory," Computational Intelligence and Neuroscience : CIN, vol. 2023, 2023. Available: https://www.proquest.com/scholarly-journals/ml-dstnet-novel-hybrid-model-breast-cancer/docview/2889071972/se-2. DOI: https://doi.org/10.1155/2023/7510419.

R. Battur and J. Narayana, "A Novel Approach for Content-based Image Retrieval System using Logical AND and OR Operations," International Journal of Advanced Computer Science and Applications, vol. 14, (9), 2023. Available: https://www.proquest.com/scholarly-journals/novel-approach-content-based-image-retrieval/docview/2883174278/se-2. DOI: https://doi.org/10.14569/IJACSA.2023.0140955.

F. Islam et al, "A Novel Method for Diagnosing Alzheimer’s Disease from MRI Scans using the ResNet50 Feature Extractor and the SVM Classifier," International Journal of Advanced Computer Science and Applications, vol. 14, (6), 2023. Available: https://www.proquest.com/scholarly-journals/novel-method-diagnosing-alzheimer-s-disease-mri/docview/2843254696/se-2. DOI: https://doi.org/10.14569/IJACSA.2023.01406131.

R. Arevalo-Ancona and M. Cedillo-Hernandez, "Zero-Watermarking for Medical Images Based on Regions of Interest Detection using K-Means Clustering and Discrete Fourier Transform," International Journal of Advanced Computer Science and Applications, vol. 14, (6), 2023. Available: https://www.proquest.com/scholarly-journals/zero-watermarking-medical-images-based-on-regions/docview/2843253774/se-2. DOI: https://doi.org/10.14569/IJACSA.2023.0140662.

E. Zaitseva et al, "A New Fuzzy-Based Classification Method for Use in Smart/Precision Medicine," Bioengineering, vol. 10, (7), pp. 838, 2023. Available: https://www.proquest.com/scholarly-journals/new-fuzzy-based-classification-method-use-smart/docview/2842974757/se-2. DOI: https://doi.org/10.3390/bioengineering10070838.

L. Kumar et al, "Robust Medical Image Watermarking Scheme Using PSO, LWT, and Hessenberg Decomposition," Applied Sciences, vol. 13, (13), pp. 7673, 2023. Available: https://www.proquest.com/scholarly-journals/robust-medical-image-watermarking-scheme-using/docview/2836331297/se-2. DOI: https://doi.org/10.3390/app13137673.

A. Pati et al, "CanDiag: Fog Empowered Transfer Deep Learning Based Approach for Cancer Diagnosis," Designs, vol. 7, (3), pp. 57, 2023. Available: https://www.proquest.com/scholarly-journals/candiag-fog-empowered-transfer-deep-learning/docview/2829795486/se-2. DOI: https://doi.org/10.3390/designs7030057.

Shrivastava, A., Chakkaravarthy, M., Shah, M.A..A Novel Approach Using Learning Algorithm for Parkinson’s Disease Detection with Handwritten Sketches. In Cybernetics and Systems, 2022

Shrivastava, A., Chakkaravarthy, M., Shah, M.A., A new machine learning method for predicting systolic and diastolic blood pressure using clinical characteristics. In Healthcare Analytics, 2023, 4, 10021

Shrivastava, A., Chakkaravarthy, M., Shah, M.A.,Health Monitoring based Cognitive IoT using Fast Machine Learning Technique. In International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(6s), pp. 720–72

Shrivastava, A., Rajput, N., Rajesh, P., Swarnalatha, S.R., IoT-Based Label Distribution Learning Mechanism for Autism Spectrum Disorder for Healthcare Application. In Practical Artificial Intelligence for Internet of Medical Things: Emerging Trends, Issues, and Challenges, 2023, pp. 305–321

Boina, R., Ganage, D., Chincholkar, Y.D., .Chinthamu, N., Shrivastava, A., Enhancing Intelligence Diagnostic Accuracy Based on Machine Learning Disease Classification. In International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(6s), pp. 765–774

Shrivastava, A., Pundir, S., Sharma, A., ...Kumar, R., Khan, A.K. Control of A Virtual System with Hand Gestures. In Proceedings - 2023 3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023, 2023, pp. 1716–1721

G. Senel et al, "Unraveling Segmentation Quality of Remotely Sensed Images on Plastic-Covered Greenhouses: A Rigorous Experimental Analysis from Supervised Evaluation Metrics," Remote Sensing, vol. 15, (2), pp. 494, 2023. Available: https://www.proquest.com/scholarly-journals/unraveling-segmentation-quality-remotely-sensed/docview/2767301974/se-2. DOI: https://doi.org/10.3390/rs15020494.

H. Wang et al, "Identification of Stripe Rust and Leaf Rust on Different Wheat Varieties Based on Image Processing Technology," Agronomy, vol. 13, (1), pp. 260, 2023. Available: https://www.proquest.com/scholarly-journals/identification-stripe-rust-leaf-on-different/docview/2767124414/se-2. DOI: https://doi.org/10.3390/agronomy13010260.

J. He and C. Li, "Research on Digital Image Intelligent Recognition Method for Industrial Internet of Things Production Data Acquisition," Traitement Du Signal, vol. 39, (6), pp. 2133-2139, 2022. Available: https://www.proquest.com/scholarly-journals/research-on-digital-image-intelligent-recognition/docview/2807004827/se-2. DOI: https://doi.org/10.18280/ts.390626.

K. J. Saudagar, "Neuro-fuzzy image compression using differential pulse code modulation and probabilistic decision making," Multimedia Tools Appl, vol. 81, (29), pp. 41929-41951, 2022. Available: https://www.proquest.com/scholarly-journals/neuro-fuzzy-image-compression-using-differential/docview/2740204860/se-2. DOI: https://doi.org/10.1007/s11042-022-13522-7.

Z. Liu et al, "A robust encryption watermarking algorithm for medical images based on ridgelet-DCT and THM double chaos," Journal of Cloud Computing, vol. 11, (1), 2022. Available: https://www.proquest.com/scholarly-journals/robust-encryption-watermarking-algorithm-medical/docview/2719936368/se-2. DOI: https://doi.org/10.1186/s13677-022-00331-4.

R. Karunya and K. A. Abdul, "Multi-orientation local ternary pattern-based feature extraction for forensic dentistry," EURASIP Journal on Image and Video Processing, vol. 2022, (1), 2022. Available: https://www.proquest.com/scholarly-journals/multi-orientation-local-ternary-pattern-based/docview/2663829019/se-2. DOI: https://doi.org/10.1186/s13640-022-00584-8.

S. N. Mallela and B. S. Rao, "An Outlook of Medical Image Analysis via Transfer Learning Approaches," Traitement Du Signal, vol. 39, (5), pp. 1463-1474, 2022. Available: https://www.proquest.com/scholarly-journals/outlook-medical-image-analysis-via-transfer/docview/2807004855/se-2. DOI: https://doi.org/10.18280/ts.390502.

S. Rajkumar et al, "A comprehensive survey on image enhancement techniques with special emphasis on infrared images," Multimedia Tools Appl, vol. 81, (7), pp. 9045-9077, 2022. Available: https://www.proquest.com/scholarly-journals/comprehensive-survey-on-image-enhancement/docview/2642112154/se-2. DOI: https://doi.org/10.1007/s11042-021-11250-y.

C. Kaushal et al, "A Framework for Interactive Medical Image Segmentation Using Optimized Swarm Intelligence with Convolutional Neural Networks," Computational Intelligence and Neuroscience : CIN, vol. 2022, 2022. Available: https://www.proquest.com/scholarly-journals/framework-interactive-medical-image-segmentation/docview/2709597113/se-2. DOI: https://doi.org/10.1155/2022/7935346.

[M. Ahmad et al, "Fuzzy Based Hybrid Focus Value Estimation for Multi Focus Image Fusion," Computers, Materials, & Continua, vol. 71, (1), pp. 735-752, 2022. Available: https://www.proquest.com/scholarly-journals/fuzzy-based-hybrid-focus-value-estimation-multi/docview/2604991615/se-2. DOI: https://doi.org/10.32604/cmc.2022.019691.




How to Cite

Saxena, P. ., Saxena, V. ., Basvant, M. S. ., Lohumi, Y. ., Saraswat, M. ., Sankhyan, A. ., Deepak, A. ., & Shrivastava, A. . (2024). Fuzzy-Based Medical Image Processing and Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 320–327. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4825



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