Melanoma is one of the deadliest types of skin cancer and can be difficult to treat when it's advanced. To reduce mortality rates, early detection is key. In order to do this, computer-aided systems have been developed to help dermatologists diagnose the condition. To make it more accessible to the public, researchers are working on creating portable, at-home diagnostic systems. An Android-based smartphone application utilizing image capture, preprocessing, and segmentation was developed to extract Asymmetry, Border irregularity, Color variegation, and Diameter (ABCD) features from skin lesions. Using these feature sets and support vector machines, the application can accurately classify malignant and benign cases. Processing an image takes under a second and the system's performance metrics (sensitivity, specificity, accuracy, and AUC) are competitive with or better than current methods. What's more, the user-friendly application is easy to download and navigate, which is key to making medical diagnosis more democratic.
Pandey, S. (2023). Skin Cancer Diagnostics. Afribary. Retrieved from https://track.afribary.com/works/skin-cancer-diagnostics
Pandey, Santosh "Skin Cancer Diagnostics" Afribary. Afribary, 18 Feb. 2023, https://track.afribary.com/works/skin-cancer-diagnostics. Accessed 27 Nov. 2024.
Pandey, Santosh . "Skin Cancer Diagnostics". Afribary, Afribary, 18 Feb. 2023. Web. 27 Nov. 2024. < https://track.afribary.com/works/skin-cancer-diagnostics >.
Pandey, Santosh . "Skin Cancer Diagnostics" Afribary (2023). Accessed November 27, 2024. https://track.afribary.com/works/skin-cancer-diagnostics