The number of people diagnosed with skin cancer is increasing sharply. Both invasive and non-invasive methods of examination may be used to investigate it. However, the invasive method is more difficult for the patient because samples must be taken from the lesion itself, or the whole lesion must be cut out. It also requires more time and cost. To avoid invasive procedures, computer-based analysis and diagnosis have the potential to increase diagnostic accuracy and turnaround time. This study develops a unique discriminative deep learning architecture (DDLA) for dermoscopic image classification (DIC), called DDLA-DIC, which uses the concept of inception. Using this concept, the proposed DDLA-DIC system is designed wider and deeper and the network learns from various spatial patterns. The proposed DDLA-DIC system can extract image characteristics from dermoscopic images for skin cancer diagnosis in an effective and efficient way. The proposed DDLA-DIC system is evaluated by utilizing the dermoscopic images from the PH2 database, and the obtained classification results are based on a random split approach. The simulation results indicate that the framework has a great deal of potential with 99.79% accuracy.
The incidence of skin cancer is rapidly increasing worldwide. The relevance of Skin Cancer Diagnosis (SCD) and the difficulty in achieving an accurate and consistent diagnosis have resulted in significant research interest. Furthermore, automated detection or classification would be even more helpful in a diagnostic assistance system. This study develops an efficient Dermoscopic Image Classification Network (DermICNet) for automated SCD. The proposed DermICNet is a deep learning architecture with an efficient arrangement of eight convolutional layers with small-sized convolution filters (3x3). The extracted features from the convolution layers are fed to the dense layer for classification. It consists of a neural network that uses stochastic gradient descent optimization to find the optimal solution for SCD. Finally, a softmax classifier is employed to classify the patterns in the dermoscopic images. The proposed DermICNet is assessed using PH2 database images. The classification results reported are based on the random-split (70:30) approach, which divides the PH2 database into training and testing. It is demonstrated that it is feasible to discriminate between abnormal and normal dermoscopic images with an average accuracy of 99.2% using the proposed DermICNet. The results suggest that the analysis of dermoscopic images using DermICNet has the potential as a diagnostic tool for SCD.
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