Computer-Aided Detection of Skin Cancer Detection from Lesion Images Via Deep Learning Techniques: 3d CNN Integrated Inception V3 Networks
Keywords:
Autoencoder,, CNN, CLAHE, FMM, Frangi vessel filter, Inception v3 Lesion, Melanoma, Transfer learningAbstract
A rising number of hereditary and metabolic peculiarities leads to malignant growth, in most parts of the body. The spread of destructive cells might be risky in any area of the body. One of the most well-known diseases is skin malignant development, and its recurrence is spreading around the globe. The three primary subtypes of skin cancer are squamous, basal cell, and melanoma, with melanoma being the most deadly and clinically aggressive. In this way, it is essential to do skin cancer screenings. One of the most impressive techniques for quickly and effectively identifying the development of skin cancer is the use of Deep Learning (DL). As a result, some steps are employed in this research to identify harmful skin: a) Information gathering using the HAM10000 dataset which includes of 10015 lesion images, b) Preprocessing of raw Inge to avoid anomalies using techniques like CLAHE, FMM and Frangi vessel, c) feature extraction using convolutional Autoencoder and finally d) a 3D CNN network for classifications and transfer learning with an Inception V3-trained model to increase the network's knowledge. Experimental evidence demonstrates that, on a variety of metrics, the suggested algorithm performs more effectively than other state-of-the-art systems (accuracy:0.96, sensitivity:0.97, specificity:0.97).
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