Abstract:
One of the most dangerous types of cancer is skin cancer, it becomes a significant health
hazard when not treated and detected on time. Skin cancer may spread to other parts of
the body and complicate treatment if it is not detected in its early stages. Mainly it is
the result of abnormal skin cell growth, usually the cells are stimulated by the sun for a
long time. The early detection of skin tumors is a basic but highly complicated and
expensive process due to the complexity of the diagnostic methods implicated.
The identification of skin cancer by the location and cells involved augments the
necessity of a very precise classifier for a successful diagnosis. Where the use of CNN
in the recognition and classification of skin cancer, especially in skin lesion
classification has been proposed to solve this issue. The utilized diagnosing method
includes the utilization of image processing algorithms and deep learning models to
increase accuracy and efficiency. Methods like image augmentation are then used for
adding more rows to the dataset are used to scale up the dataset. This way, the model
understands the diverse cases encountered. In addition, transfer learning is useful for
increasing the classification accuracy by using pre-trained models for improved
performance. As one of deep learning's deep architectures, CNNs serves as a key player
in the extraction of features and in the classification of skin problems like psoriasis.
This technique has been impressively productive for it gets a hit rate of 75%, thus
revealing future prospects in the medical field.
