International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Melanosis Detection using Machine Learning from Basal Cell Carcinoma

Author(s) Mr. Ashwin Raju Dhanorkar, Dr. Amit K. Gaikwad
Country India
Abstract Basal cell carcinoma (BCC) is the most common type of skin cancer, and early detection is crucial for successful treatment. Dermoscopy is a widely used technique for the diagnosis of skin lesions, which provides high-resolution images of the skin surface. However, the diagnostic process is still based on the visual inspection of the images by a dermatologist, which can be subject to human error. In recent years, machine learning techniques, particularly convolutional neural networks (CNNs), have been applied to the analysis of dermoscopic images with promising results. In this Paper, we propose a method for detecting BCC using CNNs on dermoscopic images of melanocytic skin lesions. Melanosis detection from basal cell carcinoma is a crucial task in the field of dermatology. In this Paper, we proposed a machine learning-based approach for the automatic detection of melanosis from basal cell carcinoma. We collected a dataset of dermoscopic images of basal cell carcinoma lesions, and our proposed method extracts features from these images and trains a deep learning model to detect melanosis.
Keywords data-set, loss, TensorFlow, convolutional neural network, hypothesis, neural network, skindisease, optimizer
Field Engineering
Published In Volume 5, Issue 3, May-June 2023
Published On 2023-05-02
Cite This Melanosis Detection using Machine Learning from Basal Cell Carcinoma - Mr. Ashwin Raju Dhanorkar, Dr. Amit K. Gaikwad - IJFMR Volume 5, Issue 3, May-June 2023. DOI 10.36948/ijfmr.2023.v05i03.4163
DOI https://doi.org/10.36948/ijfmr.2023.v05i03.4163
Short DOI https://doi.org/gsd483
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