Melanoma Espial Employing Deep Learning Applied to Mobilenet

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Abstract: The most recent advancements in smartphone-based skin cancer diagnosis software enable convenient, portable methods for melanoma risk assessment and diagnosis for skin cancer early detection due to the issue of trade-offs. [12] The majority of skin cancer detection applications do the image analysis on the server due to the (time complexity and error rate) limitations of running a machine learning algorithm for image analysis on a smartphone. In this work, we use the MobileNet v2 deep learning model to examine the performance of skin cancer picture identification and classification on Android devices. [10] We evaluate the effectiveness of a number of factors, including parameter setup, picture acquisition technique, computer and Android-based image analysis, and object identification and classification methods. Melanoma and skin cancer are utilized to evaluate the effectiveness of the suggested approach. [15] The measurement uses the testing techniques' running times, sensitivity, accuracy, and specificity. The optimum parameter for the MobileNet v2 model on Android utilizing photos from the smartphone camera gives 91.346% accuracy for classification, according to the experiment's findings. The Android app's ability to recognize objects and classify them made it possible to analyze skin cancer. Android-based image analysis maintains the computation time threshold that indicates user comfort and matches the computer's performance accuracy for high-quality photos. [18] These results drove the creation of an Android app for disease detection processing that makes use of the camera on smartphones and seeks to achieve high-accuracy real-time detection and classification.