Journal ArticleEFFICIENT SYSTEM DESIGN FOR NEXT GENERATION OF MEDICAL IMAGING FOR SKIN CANCER DETECTIONAkhtar, S; Patel, D; Asumadu, SRecent advances show the wide-ranging applications of machine learning for solving multi-disciplinary problems in cancer cell growth detection, modeling cancer growths and treatments, etc. There is growing interests among the faculty and students at Clayton State University to study the applications of machine learning for medical imaging and propose new algorithms based on a recently funded NSF grant proposal in medical imaging, skin cancer detection, and associated smartphone apps and a web-based user-friendly diagnosis interface. We tested many available open-source ML algorithm-based software sets in Python as applied to medical image data processing, and modeling used to predict cancer growths and treatments. We study the use of ML concepts that promote efficient, accurate, secure computation over medical images, identifying and classifying cancer cells, and modeling the cancer cell growths. In this collaborative project with another university, we follow a holistic approach to data analysis leading to more efficient cancer detection based upon both cell analysis and image recognition. Here, we compare ML based software methods and analyze their detection accuracy. In addition, we acquire publicly available data of cancer cell image files and analyze using deep learning algorithms to detect benign and suspicious image samples. We apply the current pattern matching algorithms and study the available data with possible diagnosis of cancer types.Roman Science Publications Ins.2023-11-0110649906International Journal of Applied Engineering & Technology: 2633-4828https://doi.org/2318574Apps for skin cancer detection. convolutional neural network, Melanoma detection, Skin cancer detection,National Science Foundation