One of the deadliest diseases is skin cancer, especially melanoma. The high resemblance between different skin lesions such as melanoma and nevus in the skin colour images increases the complexity of identification and diagnosis. An efficient automated early detection system for skin cancer detection is essential in order to save human lives, time, and effort. In this article, an automatic skin lesion classification system using a pretrained deep learning network and transfer learning was proposed. Here, diagnosing melanoma in premature stages, a detection system has been designed which contains the following digital image processing techniques. First, dermoscopy images of skin were taken and this is subjected to a preprocessing step for noise removal and postprocessing step for image enhancement. Then the processed image undergoes image segmentation using k-means and modified k-means clustering. Second, using feature extraction technology, Gray Level Co-occurrence Matrix, and first order statistics, characteristics are extracted. Features are selected on the basis of Harris Hawks optimization (HHO). Finally, various classifiers are used for predicting the stages and efficiency of the proposed work. Measures of well-known quantities, sensitivity, precision, accuracy, and specificity are used in assessing the efficiency of the suggested method, where higher values were obtained. Compared to the current methods, it is found that the classification rate exceeded the output of the current approaches in the performance of the proposed approach.
In video surveillance, automatic detection of the anomalies is the active research area in computer technology. Even though various video anomaly detection methods are introduced, detecting anomalous events, such as illegal actions and crimes, is a major challenging issue in video surveillance. Thus, an effective automatic video anomaly detection strategy based on the deep convolutional neural network (deep CNN) is developed in this research. Initially, the input video surveillance is passed into the spatiotemporal feature descriptor, named Histograms of Optical Flow Orientation and Magnitude. The features obtained from the descriptor provide the optical flow details with the aspect of normal patterns from the scene. These patterns are further subjected to the deep CNN, which is trained using the proposed dragonfly-rider optimization algorithm (DragROA) to assure the classification either as an anomalous activity or normal. The proposed DragROA is the combination of the standard dragonfly optimization algorithm and the standard rider optimization algorithm. The implementation of the proposed DragROA-based deep CNN is carried out using two datasets, namely anomaly detection dataset and UMN dataset; the performance is analyzed using the metrics, namely accuracy, sensitivity and specificity. From the analysis, it is depicted that the proposed method obtains the maximum accuracy, sensitivity and specificity of 0.9922, 0.9809 and 1, respectively, for the UCSD dataset.
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