Healthcare Professionals in Advancing CAD Systems for Cancer Diagnosis

Abstract

Collaboration will be key for using AI effectively in cancer care. Teamwork helps new tools stay accurate, ethical, and focused on patient needs. Ongoing talks between AI experts and healthcare workers help solve new problems and put patients first. Progress in AI for cancer diagnosis depends on teamwork to keep up with medical changes and patient needs. As AI grows in oncology, flexible strategies will be needed to ensure ethical use and the best results for patients. AI\'s future in cancer diagnosis should make care more accurate and personalized, improving outcomes and efficiency. Teamwork between AI experts and healthcare workers will reshape cancer diagnosis, making it fit each person better. Continuous collaboration keeps AI advances relevant and focused on patients. As AI becomes more common in diagnosis, it should improve personalized care and healthcare efficiency. As AI grows, customizing treatments will be key to handling cancer\'s challenges. Ongoing teamwork is needed to make sure new advances meet clinical needs and improve results.

Introduction

Collaboration between AI developers and healthcare professionals is critical for advancing computer-aided diagnosis (CAD) systems in cancer care. Such interdisciplinary partnerships ensure that AI tools align with clinical workflows, improve diagnostic accuracy, support early detection, and enable personalized treatment plans. By integrating clinical expertise with AI capabilities, CAD systems become more effective, reducing errors, enhancing patient outcomes, and increasing access to advanced care.

Key Points:

1. Role of AI in Cancer Diagnosis:

   • AI improves diagnostic accuracy by analyzing complex data, spotting details clinicians might miss, and supporting early tumor detection.

   • Personalized treatment plans are enhanced through AI-driven insights, leading to better patient outcomes and survival rates.

2. Importance of Collaboration:

   • Ongoing teamwork ensures AI solutions meet real clinical needs and are user-friendly within healthcare settings.

   • Collaboration addresses ethical concerns, including data privacy, consent, and bias, maintaining patient trust and promoting fairness.

   • Interdisciplinary partnerships accelerate innovation in CAD systems and ensure safe integration into clinical workflows.

3. Ethical and Practical Considerations:

   • Ethical oversight is essential for responsible AI use, fostering trust among patients and providers.

   • Continuous dialogue and training help developers and clinicians adapt AI tools to evolving medical practices while maintaining safety and equity.

4. Future Directions:

   • Future AI development should emphasize ethical, patient-centered designs, seamless clinical integration, and ongoing adaptation to new data and treatment needs.

   • Continuous collaboration, education, and feedback loops are essential to ensure AI advances improve cancer care responsibly and effectively.

Conclusion

Using AI ethically in cancer diagnosis is key to keeping patient trust. Putting ethics first creates a healthcare system focused on patient well-being. To use AI successfully, we need to balance new technology with strong ethics and always put patients first. This approach makes diagnoses more accurate and care more fair. As AI advances, ongoing teamwork across fields will be needed to handle new clinical and ethical challenges.

References

[1] Ibrahim, R. K. R., Y.S., N. H., Biju, A., George, J., & Varghese, T. (2024). AI in Health Care: Revolutionizing Diagnostics and Cancer Treatment.Deleted Journal. https://doi.org/10.47392/irjaem.2024.0563 [2] Sahoo, P. K., Kundu, M., & Begum, J. (2024). Artificial Intelligence in Cancer Diagnosis: A Game-Changer in Healthcare. Current Pharmaceutical Biotechnology. https://doi.org/10.2174/0113892010298852240528123911 [3] FNU, N., Zeb, S., Abbasi, N., Qayyum, M. U., & Fahad, M. (2024). AI in Healthcare: Breaking New Ground in the Management and Treatment of Cancer.Asian Journal of Engineering, Social and Health. https://doi.org/10.46799/ajesh.v3i10.453 [4] Babu, C. V. S., Mohideen, A. M., Saikrishna, K., & Kannan, K. (2024). Transforming Cancer Diagnosis.Advances in Healthcare Information Systems and Administration Book Series. https://doi.org/10.4018/979-8-3693-6294-5.ch002 [5] Akter, S. (2024).AI-Driven Precision Medicine: Transforming Personalized Cancer Treatment. https://doi.org/10.60087/vol2iisue1.p21 [6] Dlamini, Z. (2023).The Application of AI in Precision Oncology: Tailoring Diagnosis, Treatment, and the Monitoring of Disease Progression to the Patient. https://doi.org/10.1007/978-3-031-21506-3_1 [7] Aftab, M. N., Mehmood, F., Zhang, C., Nadeem, A., Dong, Z., Jiang, Y., & Liu, K. (2025).AI in Oncology: Transforming Cancer Detection through Machine Learningand Deep Learning Applications. https://doi.org/10.48550/arxiv.2501.15489 [8] Napitupulu, P. A. (2023). Ethical Dilemmas in the Use of Artificial Intelligence in Breast Cancer Diagnosis and Treatment (Addressing Issues of Bias, Transferability, and Patient Trust in Breast Cancer AI).West Science Law and Human Rights. https://doi.org/10.58812/wslhr.v1i04.314 [9] Mirnezami, R. (2020).Cancer diagnostics and treatment decisions using artificial intelligence. https://doi.org/10.1016/B978-0-12-818438-7.00005-8 [10] Healthcare Solution based on Machine Learning Applications in IOT and Edge Computing, SMKD Majumder, International Journal of Pure and Applied Mathematics 119 (16), 1473-1484, 2018 https://www.acadpubl.eu/hub/2018-119-16/1/142.pdf [11] Analysis of different wavelets for brain image classification using support vector machine, S Mohankumar, International Journal of Advances in Signal and Image Sciences 2 (1), 1-4, 2016 https://xlescience.org/index.php/IJASIS/article/view/7/11 [12] Methods And Techniques To Deal With Big Data Analytics And Challenges In Cloud Computing Environment, DSMK Naga Raju Hari Manikyam, International Journal of Civil Engineering & Technology 8 (4), 668-678, 2017 https://iaeme.com/MasterAdmin/Journal_uploads/IJCIET/VOLUME_8_ISSUE_4/IJCIET_08_04_078.pdf [13] Glaucoma Image Classification Using Entropy Feature and Maximum Likelihood Classifier, A Rebinth, SM Kumar, T Kumanan, G Varaprasad, Journal of Physics: Conference Series 1964 (4), 042075, 2021 https://iopscience.iop.org/article/10.1088/1742-6596/1964/4/042075 [14] Anisha Rebinth, S. Mohan Kumar; Channel separation with CNN model for glaucoma color spectral detection. AIP Conf. Proc. 3 October 2022; 2519 (1): 030020. https://doi.org/10.1063/5.0109768 [15] Skin Cancer Diagnostic using Machine Learning Techniques - Shearlet Transform and Naïve Bayes Classifier, KG S. Mohan Kumar, J. Ram Kumar, International Journal of Engineering and Advanced Technology 9 (2), 3478-3480, 2019 https://www.ijeat.org/wp-content/uploads/papers/v9i2/B4916129219.pdf [16] Importance of Manual Image Annotation Tools and Free Datasets for Medical Research, DSMK Anisha Rebinth, Journal of Advanced Research in Dynamical and Control System, 2019 https://www.jardcs.org/abstract.php?id=219 [17] A Symmetrically Diminished Interconnected Database Segmentation Framework Using Data Mining, SM Kumar, D Majumder, AS Naragunam, DV Ashoka, Journal of Physics: Conference Series 1964 (4), 042071, 2021 https://iopscience.iop.org/article/10.1088/1742-6596/1964/4/042071 [18] Rebinth, A., Kumar, S.M. (2021). Wavelet Packet Transform-Based Image Classification for Computer-Aided Glaucoma Diagnosis Using Naïve Bayes Classifier. In: Satapathy, S.C., Bhateja, V., Ramakrishna Murty, M., Gia Nhu, N., Jayasri Kotti (eds) Communication Software and Networks. Lecture Notes in Networks and Systems, vol 134. Springer, Singapore. https://doi.org/10.1007/978-981-15-5397-4_60 [19] Image processing-based Lung Tumor-Detection and Classification using 3D Micro-Calcification of CT Images, S Murugan, SM Kumar, TRG Babu, International Journal of MC Square Scientific Research 12 (1), 1-10, 2020 [20] A Deep Learning Approach To Computer Aided Glaucoma Diagnosis, A Rebinth, SM Kumar, International Conference on Recent Advances in Energy-efficient, 2019 [21] Statistical Features Based Classification of Microcalcification in Digital Mammogram Using Stochastic Neighbor Embedding, S MohanKumar, Balakrishnan, G, International Journal of Advanced Information Science and Technology 7 (7), 2012 [22] Skin Lesion Classification System Using Shearlets, SM Kumar, T Kumanan, Computer Systems Science & Engineering 44 (1), 2023 https://www.techscience.com/csse/v44n1/48024 [23] Wavelet and Symmetric Stochastic Neighbor Embedding based Computer Aided Analysis for Breast Cancer, GB S.Mohan Kumar, Indian Journal of Science and Technology 9 (47), 1-7, 2016 10.17485/ijst/2016/v9i47/106512 [24] Categorization of Benign and Malignant Digital Mammograms Using Mass Classification- SNE and DWT, BG S Mohan Kumar, Karpagam Journal of Computer Science 7 (4), 237-243, 2013 https://karpagampublications.com/wp-content/uploads/2017/07/article-vol7-iss5-2.pdf [25] The performance Evaluation of the Breast Mass Classification CAD system based on DWT, SNE and SVM, S MohanKumar, Balakrishnan, G, IJETAE 3 (10), 581-587, 2013 https://www.ijetae.com/files/Volume3Issue10/IJETAE_1013_95.pdf [26] Review on Importance and Advancement in Detecting Sensitive Data Leakage in Public Network, DSMK Ms. Revathi Yegappan, International Journal of Engineering Research and General Science 4 (2), 263-265, 2016 https://oaji.net/articles/2016/786-1461992767.pdf [27] Multi Resolution Analysis for Mass Classification in digital Mammogram using Stochastic Neighbor Embedding, GB S Mohan Kumar, International conference on Communication and Signal Processing, April 3-5, 2013 10.1109/iccsp.2013.6577024 [28] Melanoma Skin Cancer Classification Using Deep Learning Convolutional Neural Network, KG S. Mohan Kumar, J. Ram Kumar, 351-355, 2020 https://medicolegalupdate.org/scripts/MLU_July_2020_New_18.8.20_Final%20(1).pdf [29] The Performance Evaluation of the Breast Microcalcification CAD System Based on DWT, SNE AND SVM, S MohanKumar, Balakrishnan, G, Ciit International Journal of Digital Image Processing 5 (11), 483-487, 2013 https://www.ciitresearch.org/dl/index.php/dip/article/view/DIP112013005 [30] Classification of Micro Calcification and Categorization of Breast Abnormalities- Benign and Malignant in Digital Mammograms Using SNE and DWT, BG S Mohan Kumar, Karpagam Journal of Computer Science 7 (5), 253-259, 2013 https://kjcs.karpagampublications.com/media/submissions/article-vol7-iss5-2.pdf [31] Automated detection of Retinal Defects using image mining- A review, DSM Kumar, A Rebinth, European Journal of Biomedical and Pharmatical Sciences, 2349-8870, 2018 https://storage.googleapis.com/innctech/ejbps/article_issue/volume_5_january_issue_1/1514635588.pdf [32] A Study On Data Mining Techniques, Methods, Tools And Applications In Various Industries, SMK R. Jaya, International Journal of Current Research and Review 8 (4), 34-38, 2016 https://ijcrr.com/uploads/330_pdf.pdf [33] Ayurveda Medicine Roles in Healthcare Medicine, and Ayurveda Towards Ayurinformatics, R Jaya, S MohanKumar, International Journal of Computer Science and Mobile Computing 4 (12), 2015 https://ijcsmc.com/docs/papers/December2015/V4I12201512.pdf [34] Breast Cancer Diagnostic system based on Discrete Wavelet Transformation and stochastic neighbour Embedding, BG Mohan Kumar. S, European Journal of Scientific Research 87 (03), 301-310, 2012 [35] Factors for improving the research publications and quality metrics, DSMK Dr. V. Ilango, International Journal of Civil Engineering and Technology 8 (4), 477-496, 2017 https://iaeme.com/MasterAdmin/Journal_uploads/IJCIET/VOLUME_8_ISSUE_4/IJCIET_08_04_055.pdf [36] Medical Diagnosis Cad System Using Latest Technologies, Sensors And Cloud Computing, TRDSM Kumar, International Journal of Computer Engineering & Technology 8 (1), 43-50, 2017 https://iaeme.com/MasterAdmin/Journal_uploads/IJCET/VOLUME_8_ISSUE_1/IJCET_08_01_006.pdf [37] Classification of Breast Mass classification-CAD System with Performance Evaluation Using SSNE, S MohanKumar, Balakrishnan, G, International Journal of Advanced Research in Computer Science and Software, 2015 [38] Mammogram classification using VGG-16 architecture, E Sivanantham, P Epsiba, B Gopi, P Solainayagi, K Umapathy, SM Kumar, AIP Conference Proceedings 2523 (1), 020070, 2023 https://doi.org/10.1063/5.0111085 [39] Channel separation with CNN model for glaucoma color spectral detection, A Rebinth, SM Kumar, AIP Conference Proceedings 2519 (1), 030020, 2022 https://doi.org/10.1063/5.0109768 [40] Automated Detection of Retinal Anamolies Using Computer Aided Techniques-A Comparative Research, A Rebinth, DSM Kumar, 1st International Conference on Emerging Trends and Challenges in Applied, 2020 [41] Computer Aided Diagnostic Techniques in Automated Detection of Eye Related Diseases-A Comparative Study, A Rebinth, DSM Kumar, International Conference on Innovative Research in Engineering, Management, 2019 [42] Classification of Breast Mass classification-CAD System with Performance Evaluation, DGB Dr.S.Mohan Kumar, International Journal Of Engineering And Computer Science 4 (9), 14187-14193, 2015 https://ijecs.in/index.php/ijecs/article/view/3133 [43] Classification of Microcalcification in Digital Mammogram using Stochastic Neighbor Embedding and KNN Classifier, S MohanKumar, Balakrishnan, G, International Conference on Emerging Technology Trends on Advanced, 2012 https://research.ijcaonline.org/icett/number1/icett1002.pdf [44] Glaucomatous Image Classification CAD System Using Adaptive Wavelets, Probabilistic PCA and Random Forest Techniques Machine Learning Model, A Rebinth, DSM Kumar, International Journal Of Innovation In Engineering Research & Management https://ijierm.co.in/index.php/IJIERM/article/view/1513/1661 [45] Lo-Ra based covid patient health detecting system, D Majumder, SM Kumar, DV Ashoka, AS Naragunam, AIP Conference Proceedings 2523 (1), 020010, 2023 https://doi.org/10.1063/5.0110512 [46] Glaucoma diagnosis based on colour and spatial features using kernel SVM, A Rebinth, SM Kumar, Cardiometry, 508-515, 2022 https://doi.org/10.1063/5.0110512 [47] A Survey On Medical Data Mining- Healthcare Related Research And Challenges, DSM Kumar, International Journal of Current Research 8 (1), 25170, 2016 https://journalcra.com/sites/default/files/issue-pdf/12313.pdf [48] Prognosis and prediction of disease using hybrid machine learning framework, P Epsiba, B Gopi, K Umapathy, P Solainayagi, E Sivanantham, SM Kumar, AIP Conference Proceedings 2523 (1), 020042, 2023 https://doi.org/10.1063/5.0110840 [49] Securing Pedestrian Crosswalks in Smart Cities: An Embedded Vision System for Pedestrian Detection and Safety Enhancement, DSM Kumar, International Conference on Smart Technologies for Smart Nation, 2023 https://ieeexplore.ieee.org/document/10391796 [50] IoT - BLE Based Indoor Navigation for Visually Impaired People, DSM Kumar, International Conference on Smart Technologies for Smart Nation, 2023 https://ieeexplore.ieee.org/document/10391662 [51] Soft Computing Based Discriminator Model for Glaucoma Diagnosis, A Rebinth, SM Kumar, Computer Systems Science And Engineering 42 (3), 867-880, 2022, DOI:10.32604/csse.2022.022955 https://www.techscience.com/csse/v42n3/46731/html [52] Design of Deep Neural Architecture for Brain Cancer Classification Using Pyramid Design, SM Kumar, KP Yadav, Journal of Physics: Conference Series 1964 (7), 072021, 2021 https://iopscience.iop.org/article/10.1088/1742-6596/1964/7/072021 [53] Brain Image Classification by Deep Neural Network with Pyramid Design of Inception Module, SM Kumar, KP Yadav, Annals of the Romanian Society for Cell Biology 25 (6), 1871-1880, 2021 http://annalsofrscb.ro/index.php/journal/article/view/5726/4449 [54] Study on skin Lesion Classifications system and Dermoscopic Feature Analysis for Melanoma, Kumanan T, S Mohan Kumar, International journal of Creative Research Thoughts 6 (1), 1863 – 1873, 2018 https://ijcrt.org/papers/IJCRT1802680.pdf [55] Classification of breast microcalcification- CAD system and performance evaluation using SSNE, SMK Balakrishnan, International Journal of Advanced Research in Computer Science and Software, 2015 [56] Classification of Breast Mass Classification-CAD System and Performance Evaluation Using SSNE, K SM, Balakrishnan, International Journal of Innovative Science, Engineering & Technology 2 (9), 2015 https://ijiset.com/vol2/v2s9/IJISET_V2_I9_51.pdf [57] Dermoscopic Image Classification Using Two-Stage Processing of Shearlet Features with Support Vector Machine, SM Kumar, T Kumanan, Micro-Electronics and Telecommunication Engineering, 447, 2021 https://link.springer.com/chapter/10.1007/978-981-33-4687-1_43 [58] Skin Lesion Classification System and Dermoscopic Features Analysis for Melanoma recognition and Prevention, DSMKDT Kumanan, International Journal of Emerging Technology and Advanced Engineering 7 (8), 2018 [59] Artificial Intelligence: Foundations, Applications, and the Generative Future, DSM Kumar, 30th April 2025 ISBN: 978-93-92090-63-9, DOI: https://doi.org/10.47716/978-93-92090-63-9 [60] AI in Precision Healthcare: A New Frontier, DSMKDG Balakrishnan, ISBN: 978-93-86388-50-6, 2025 DOI: https://doi.org/10.47715/978-93-86388-50-6 [61] Industry 6.0 impediments and future trends in industries ISBN 979-8324031077, DMS Dr Mohan Kumar S, Dr Thomas M Chen, 2024 https://www.amazon.in/Industry-6-0-Impediments-Future-Industries-ebook/dp/B0D2QCWZQX [62] A distributed e-health management model with edge computing in healthcare framework, D Majumder, SM Kumar, Cardiometry, 444-455, 2022 https://cardiometry.net/issues/no22-may-2022/distributed_e-health_management [63] Features with Support Vector Machine, SM Kumar, T Kumanan, Micro-Electronics and Telecommunication Engineering: Proceedings, 2021 [64] Medical Image Augmentation and Enhancement using Machine Learning, DKPY Dr. S Mohan Kumar, 978-93-91303-40-2, 2021 [65] An Edge Based Smart Healthcare Model with Machine Learning Approaches, DSMK Darpan Majumder, Design Engineering 0011-9342 (09), Pages: 9214 - 9229, 2021 [66] Deep Learning-Based MRI Brain Tumor Classification Using Convolutional Neural Network Model, KPY, S Mohan Kumar, Design Engineering, 900-909, 2021 [67] An Enhanced Convolutional Neural Architecture With Residual Module For Mri Brain Image Classification System, SM Kumar, KP Yadav, Turkish Journal of Physiotherapy and Rehabilitation 32 (3), 911-917, 2021 [68] Melanoma Skin Cancer Classification Using Deep Learning Convolutional Neural Network, KG S. Mohan Kumar, J. Ram Kumar, Indian Journal of Public Health Research & Development 20 (3), 351-355, 2020 [69] Deep Learning Architectures and their Application to MRI Brain Image Classification, DKPY Dr. S Mohan Kumar, ISBN 978-81-952262-2-1 [70] Research Methodology, Prof. (Dr.) S. Mohan Kumar, 978-93-91303-73-0 https://doi.org/10.47715/JPC.B.978-93-91303-73-60 [71] Research and Publication Ethics, Prof. (Dr.) S. Mohan Kumar, 978-93-92090-28-8 www.doi.org/10.47716/MTS.B.978-93-92090-28-8 [72] Mohankumar.S, Kavita Bhatt.,(2022). Medical Image Augmentation and Enhancement using Machine Learning and Deep Learning (1st ed., pp. 1-148). Jupiter Publications consortium,ISBN:978-93-91303-40-2, DOI: https://doi.org/10.47715/JPC.978-93-91303-40-2 [73] Deep Learning Architectures and their Application to MRI Brain Image Classification,Dr.S.Mohan Kumar, Prof.(Dr.)K.P.Yadav, 978-81-952262-2-1 [74] Machine Learning and IoT for Intelligent Systems and Smart Applications, ISBN 9781003194415, Mohan Kumar S., T. Kumanan, T. R. Ganesh Babu, S. Poovizhi E-ISBN 9781003194415 https://doi.org/10.1201/9781003194415 [75] Mohan Kumar, S., and G. Balakrishnan. AI in Precision Healthcare: A New Frontier. Jupiter Publications Consortium, 2025. DOI: https://doi.org/10.47715/978-93-86388-50-6. [76] Kumar, S. M. (2024, April 30). Artificial Intelligence: Foundations, Applications, and the Generative Future. Magestic Technology Solutions (P) Ltd. https://doi.org/10.47716/978-93-92090-63-9 [77] Importance of manual image annotation tools and free datasets for medical research, Mohan Kumar, S., Rebinth, A., Journal of Advanced Research in Dynamical and Control Systems, 11(1 Special Issue), pp. 1168-1176, 2019 https://www.jardcs.org/abstract.php?id=219 [78] Melanoma skin cancer classification using deep learning convolutional neural network, Medico Legal Update, 20(3), pp. 351-355, Mohan Kumar, S., Ram Kumar, J., Gopalakrishnan, K., 2020 https://doi.org/10.37506/mlu.v20i3.1421 [79] Mohan Kumar, S., Kumanan, T. (2023). Skin Lesion Classification System Using Shearlets. Computer Systems Science and Engineering, 44(1), 833–844. https://doi.org/10.32604/csse.2023.022385 [80] A distributed e-health management model with edge computing in healthcare framework, Majumder, D and Kumar, SM, May 2022, Cardiometry (22), pp.444-455 10.18137/cardiometry.2022.22.444455 [81] Glaucoma diagnosis based on colour and spatial features using kernel SVM, Rebinth, A and Kumar, SM, May 2022, CARDIOMETRY (22), pp.508-515 10.18137/cardiometry.2022.22.508515 [82] Rebinth, A., Mohan Kumar, S. (2022). Soft Computing Based Discriminator Model for Glaucoma Diagnosis. Computer Systems Science and Engineering, 42(3), 867–880. https://doi.org/10.32604/csse.2022.022955 [83] A Deep Learning Approach To Computer Aided Glaucoma Diagnosis, Mohan Kumar, S., Rebinth, A., 2019 International Conference on Recent Advances in Energy Efficient Computing and Communication, ICRAECC 2019 10.1109/ICRAECC43874.2019.8994988 [84] Rebinth, A., Kumar, S.M. (2021). Wavelet Packet Transform-Based Image Classification for Computer-Aided Glaucoma Diagnosis Using Naïve Bayes Classifier. In: Satapathy, S.C., Bhateja, V., Ramakrishna Murty, M., Gia Nhu, N., Jayasri Kotti (eds) Communication Software and Networks. Lecture Notes in Networks and Systems, vol 134. Springer, Singapore. https://doi.org/10.1007/978-981-15-5397-4_60 [85] A Symmetrically Diminished Interconnected Database Segmentation Framework Using Data Mining, Journal of Physics Conference Series, 1964(4), Mohan Kumar, S., Majumder, D., Shajin Naragunam, A., Ashoka, D.V., 2021 https://iopscience.iop.org/article/10.1088/1742-6596/1964/4/042071 [86] Glaucoma Image Classification Using Entropy Feature and Maximum Likelihood Classifier, Kumar, S.M., Rebinth, A., Kumanan, T., Varaprasad, G., Journal of Physics Conference Series, 1964(4), 2021 https://iopscience.iop.org/article/10.1088/1742-6596/1964/4/042075 [87] Data-Driven with IoT Sensing and Deep Learning Model for Dynamic Skin Cancer Diagnosis, 2024, Kumar, S.M., Thenmozhi, R., Balaji Damodhar, T.S., Malathi, N., Meenakshi, B., 2nd International Conference on Self-Sustainable Artificial Intelligence Systems ICSAS 2024, Proceedings, pp. 1034-1039. 10.1109/ICSSAS64001.2024.10760605 [88] S. Mohan Kumar, J. B. Jesudasan Peter, S. Kolangiammal, A. Mubarakali, S. Karthik and S. Sujatha, \"Cloud-Powered Healthcare Appointment Optimization with Reinforcement Learning for Efficiency,\" 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI), Coimbatore, India, 2024, pp. 229-235, 10.1109/ICoICI62503.2024.10696195 [89] S. M. Kumar and G. Balakrishnan, \"Multi Resolution Analysis For Mass Classification In Digital Mammogram Using Stochastic Neighbor Embedding,\" 2013 International Conference on Communication and Signal Processing, Melmaruvathur, India, 2013, pp. 101-105, 10.1109/iccsp.2013.6577024 [90] Channel Separation for glaucoma Color Spectral Detection, S Murugan, SM Kumar, TRG Babu, International Journal of MC Square Scientific Research 12 (2), 1-10, 2020 [91] Device for Detection of Melanoma Skin Cancer Using AI, 376924-001, S Mohan Kumar, T Y SATHEESHA, Amit Kumar K [92] Patient Health Monitoring Device, 382543-001, Mohd.Wazih Ahmad, Taranath N L, Roopa H, S Mohan Kumar [93] Smart Wearable Device for Monitoring and Managing Postpartum Stress Disorder in Females, 387824-001, Kavitha Bhatt, S Mohan Kumar [94] Intelligent Wireless Device for Detection of Cancerous Cells, 387005-001, Shilpa Bhairanatti, S Mohan Kumar

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Copyright © 2025 R. Sakthivel. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.