PROFESSIONAL EXPERIENCE
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Position Organization Period
Professor K.I.T.C.O.E. Kolhapur 8th Dec. 2017 to till date
Professor &HoD SGI Atigre 18th July 2016 to 7th
I/C Principal Bharati Vidyapeeth’s College of Engineering, Kolhapur from 21st Oct. 2009 11th Nov. 2010
Principal Bharati Vidyapeeth’s College of Engineering, Kolhapur 12th Nov. 2010 to 16th July. 2016
HOD & Asst. Professor Bharati Vidyapeeth’s College of Engineering, Kolhapur 15th July2003 to 20thOct. 2009
Lecturer K.I.T.C.O.E. Kolhapur Jan 1997 to 14th July2003
V. Lecturer New Poly-technique Kolhapur 15-11-95 to Jan 1997
Hardware Engineer Hard-Core Kolhapur Jan 1995 to Jan 1997
EDUCATION
Ph.D. in Electronics
Master of Engineering in Electronics (ME)
BE in Electronics
RESEARCH INTERESTS
Image processing, Digital signal processing
18
Scopus Publications
Scopus Publications
FastColitisDetector-XAI: An efficient AI model utilizing sparse Autoencoder with explainable AI for ulcerative colitis diagnosis Sumedh Vithalrao Dhole, Sangeeta R. Chougule Methodsx, 2025 We suggest AI framework for UC diagnosis using Sparse Autoencoders (SA) for feature extraction combined with Explainable AI (XAI) utilizing Grad-CAM to provide a higher degree of interpretability for the model. SA is applied for dimensionality reduction of medical images to efficiently encode the images and preserve vital information required for diagnosis. Features so extracted are passed to a machine learning classifier for classification for detection of UC presence. Visualizations from Grad-CAM are utilized to demarcate areas critical for disease, like regions of inflammation, ulcers and mucosal patterns, so as to achieve transparency and also allow the clinicians to see why the model did what it did. The proposed SA-XAI model greatly surpasses competing models in their respective performance in accuracy, precision, recall and F1 score with remarkable results of 98 %, 97.5 %, 96.4 % and 95 % respectively. Coupling of Sparse Autoencoders with XAI, achieves high accuracy in diagnosis and gains clinician's confidence in AI model's decision-making transparent. Methodology include:•Sparse Autoencoders to extract and condense the most salient features from medical images.•Grad-CAM to highlight significant regions, which maintains model's decision making process transparent.•Has 98 % accuracy, 97.5 % precision, 96.4 % recall and 95 % F1 score for detecting UC.
Advancing Skin Disease Diagnosis: A Review of Deep Learning Approaches, Datasets and Challenges 16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
Review on Steganography Techniques used for Medical Imagining 16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
Identification and Categorization of Apple Fruit Disorders 16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
Analysis of Ulcerative Colitis Detection from Encoscopic Video Sumedh Vithalrao Dhole, Sangeeta R. Chougule Proceedings of the 2025 12th International Conference on Computing for Sustainable Global Development Indiacom 2025, 2025 In this work, endoscopic video analysis is used to diagnose ulcerative colitis by combining coarse-grained and finegrained information. Motivated by the progress in detecting malignancies from microscopic pictures, we investigate the effectiveness of deep learning models, including sophisticated models like MobileNet-V3, Inception-V3, and EfficientNet-B0. We show that these models are effective in classifying various forms of ulcerative colitis, which holds promise for improved sensitivity, specificity, and accuracy in automated disease detection systems. Drawing on a comprehensive examination of sensitivity and specificity, we emphasize how these models frequently strike a compromise between a low probability of false positives and negatives and fewer negative and more positive identifications. The relevance of precise disease forecasts and customized therapies for specific ulcerative colitis subtypes are underscored by these advanced findings. Our findings indicate that reliable techniques for diagnosing ulcerative colitis have the potential to completely transform the medical field. Because of the increased precision in sickness evaluation that these advancements provide, they may enhance diagnostic accuracy and mitigate the consequences of delayed or incorrect diagnosis. Our work improves the healthcare elements through better diagnostic apps for ulcerative colitis and insights into sustainable procedures.
A Review on Face Emotion Recognition using EEG Features and Facial Features Sarvajeet A. Bhosale, Sangeeta R. Chougule 2023 1st International Conference on Cognitive Computing and Engineering Education Icccee 2023, 2023 Emotions recognition using feature extraction from face, Speech and EEG signal have become emerging research field. It has contributed in different research areas like safety, biomedical sector, industrial automation and Human Computer Interface. Deep Learning approach using Convolution Neural Networks (CNN) has provided better results in obtaining an accuracy in Facial Emotion Recognition. Researcher are trying to get better results by using a novel approach along with CNN’s to obtain better results. This paper will help the researchers to understand the novel approach in Deep Learning used for Facial Emotion Recognition (FER) with different techniques and Electroencephalogram (EEG) based emotion recognition classified on basis of Valence, Arousal and Dominance with different datasets like DEAP (Database of Emotion Analysis using Physiological signals), SEED (SJTU Emotion EEG Dataset). The review provided use of different dataset and the accuracy of models while using those dataset along with the novel approaches in Emotion Recognition. This paper also reviews different algorithms, architectures and recent work carried by different researchers.
1. International Journal of Applied Engineering Research ISSN 0973-4562 Volume 3, Number 12 (2008), pp. 1705–1718 Investigation Of An Oversampled Filter Bank Near Perfect Reconstruction Of Input Image.
2. International Journal of engineering research and industrial applications(IJERIA),ISSN 0974-1518, (2008),pp 105-120 Oversampled FIR Filter banks design without aliasing for 2-D image
3. International Journal of Mathematical sciences and engineering applications(IJMSEA),ISSN 0973-9424,Vol.2 An oversampled FIR filter bank design with uniform and nonuniform subsampled factor for 2D image
4. International Journal of Applied Engineering Research ISSN 0973-4562,volume 3,Number 3 (2008), An Oversampled FIR Filter Bank Design with Uniform and Nonuniform Frequency bands for 2-D Image
5. Adit journal of engineering,vol 4, journal GujratDec. 2008 FIR Filter Banks Design With Different Susubsampled Factors For 2D Image
6. International Conference on Signal Processing,Communication and Networking(ICSCN 2008) Design of oversampled filter banks for 2D-D image
7. International MultiConference of Engineers and Computer Scientists (IMECS) 2011, March 16-18, 2011 Vol. I ,Hong Kong Investigation of Three Channels Oversampled FIR Filter Bank
8. Second International Conference,UCMA 2011 Proceedings Part-I Daejeon,Korea, Oversampled Perfect Reconstruction FIR Filter Bank Implemen
