An Efficient Method to Minimize the Depth Estimation Error in Melanoma Skin Cancer Classification

Autor: K Amit Kumar, T Y Satheesha

Publikováno v: 2022 4th International Conference on Circuits, Control, Communication and Computing (I4C).

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8bbbb8235614dab5cd177a460bb26e8d
https://doi.org/10.1109/i4c57141.2022.10057697

Melanoma Is Skin Deep: A 3D Reconstruction Technique for Computerized Dermoscopic Skin Lesion Classification

Autor: D. Satyanarayana, Kashyap D. Dhruve, M. N. Giri Prasad, T. Y. Satheesha

Publikováno v: IEEE Journal of Translational Engineering in Health and Medicine, Vol 5, Pp 1-17 (2017)
IEEE Journal of Translational Engineering in Health and Medicine

Melanoma mortality rates are the highest amongst skin cancer patients. Melanoma is life threating when it grows beyond the dermis of the skin. Hence, depth is an important factor to diagnose melanoma. This paper introduces a non-invasive computerized

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ca79e96926b19917bedb28cf22e69e4d
https://ieeexplore.ieee.org/document/7819474/

Proposed Threshold Algorithm for Accurate Segmentation for Skin Lesion

Autor: T. Y. Satheesha, D. Sathyanarayana, M. N. Giri Prasad

Automated diagnosis of skin cancer can be easily achieved only by effective segmentation of skin lesion. But this is a highly challenging task due to the presence of intensity variations in the images of skin lesions. The authors here, have presented

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::beb5e5d7f79a52927e4541e6cd333422
https://doi.org/10.4018/978-1-5225-0549-5.ch009

Extraction of melanoma 3d features from tensor representation

Autor: T Y Satheesha, Sushmita N S, Gururaj Murtugudde

Publikováno v: International Journal of Engineering & Technology. 7:373

Melanoma is one of the unsafe growth to be dealt with too to recognize in introductory stage. Here we take the skin sore by ROI and after that we take out highlights of it then it should be sectioned whether the specific picture is malignant or not.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::36663ed0e9874fc8df99a87aced4000c
https://doi.org/10.14419/ijet.v7i1.9.10012

Trained neural networking framework based skin cancer diagnosis and categorization using grey wolf optimization.

Autor: K AK; School of Engineering, CMR University, Bengaluru, India., T Y S; School of Computer Science and Engineering, REVA University, Bengaluru, India., Ahmed ST; Department of Electrical Engineering, Indian Institute of Technology Hyderabad, Hyderabad, India. syed.edu.in@gmail.com., Mathivanan SK; School of Computer Science and Engineering, Galgotias University, Greater Noida, 203201, India., Varadhan S; Department of Computer Applications, Dr. MGR Educational and Research Institute, Chennai, 600095, India., Shah MA; Kebri Dehar University, Kebri Dehar, Somali, 250, Ethiopia. drmohdasifshah@kdu.edu.et.; Division of Research and Development, Lovely Professional University, Phagwara, Punjab, 144001, India. drmohdasifshah@kdu.edu.et.

Publikováno v: Scientific reports [Sci Rep] 2024 Apr 24; Vol. 14 (1), pp. 9388. Date of Electronic Publication: 2024 Apr 24.