The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Proposed System for Remote Detection of Skin Diseases Using Artificial Intelligence
Open accessAbstract
Skin diseases are prevalent diseases with visible symptoms and affect around 900 million of people in the world at any time. More than a half of the population is affected by it at an indefinite time. Dermatology is uncertain, unfortunate and strenuous to diagnose due to its complications. In the dermatology field, many times thorough testing is carried out to decide or detect the skin condition the patient may be facing. This may vary over time on practitioner to practitioner. This is also based on the person’s experience too. Hence, there is a need for an automated system which can help a patient to diagnose skin diseases without any of these constraints. We propose an image based automated system for recognition of skin diseases using Artificial intelligence. This system will make use of different techniques to analyze and process the image data based on various features of the images. Since skin diseases have visible symptoms, we can use images to identify those diseases. Unwanted noise is filtered and the resulting image is processed for enhancing the image. Complex techniques are used for feature extraction such as Convolutional Neural Network (CNN) followed by classifying the image based on the algorithm of softmax classifier. Diagnosis report is generated as an output. This system will give more accurate results and will generate them faster than the traditional method, making this application more efficient and dependable. This application can also be used as a real time teaching tool for medical students in the dermatology domain.
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“These models have been proven to provide high sensitivity in the identification of malignant tumors and differentiation of various skin diseases thus being useful for dermatologists (6, 39, 40).”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [40]
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Citations
Cites
A Method Of Skin Disease Detection Using Image Processing And Machine Learning
ALEnezi, N. S. A. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.12.090 · Cited by 266
Diagnosis of skin diseases using Convolutional Neural Networks
Rathod, J., Waghmode, V., Sodha, A., & Bhavathankar, P. (2018) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca.2018.8474593 · Cited by 139
Digital dermatology: Skin disease detection model using image processing
Ajith, A., Goel, V., Vazirani, P., et al. (2017) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iccons.2017.8250703 · Cited by 42
Image Analysis Model For Skin Disease Detection: Framework
Haddad, A., & Hameed, S. A. (2018) · International Conference on Computer and Communication Engineering · doi:10.1109/iccce.2018.8539270 · Cited by 35
Meiburger, K. M., Savoia, P., Molinari, F., Veronese, F., Tarantino, V., Salvi, M., Fadda, M. L., Seoni, S., Zavattaro, E., Santi, B. D., et al. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856720 · Cited by 2
Research works printed but not linked: 1
- Nawal Soliman ALKolifi ALEnezi: A Method Of Skin Disease Detection Using Image Processing And Machine Learning, Procedia Computer Science. Volume 163, 2019, pp 85-92, (2019). · DOI
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Sorted by citations
Revolutionizing dermatopathology using AI in skin diagnostics: scoping review
Rammal, R., Mohy U Din, A., & Alam, T. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1614681 · Cited by 1
Metadata
- Authors
- Chaware, S. M., Deshpande, A., Palkar, A., Bahire, D., & Singh, R.
- Affiliations
- Marathwada Mitra Mandal's College of Engineering (IN)
- Venue
- International Journal of Scientific Research in Computer Science Engineering and Information Technology
- Publisher
- Technoscience Academy
- Published
- 2021-04-07
- Pages
- 263-267
- ISSN
- 2456-3307 (ISSN-L)
- OpenAlex
- W3158302226
- Cited by
- 1
- References
- 5
- Citations by year
- 2026: 1
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- International Journal of Scientific Research in Computer Science Engineering and Information Technology (publishedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (cc-by)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- skin disease detection, dermatology, automated diagnosis, diagnostic imaging, convolutional neural networks, image feature extraction, image noise filtering, image enhancement, softmax classifier
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