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Malignant melanoma, not belongs to a common type of skin cancers but most serious because of its growth—affecting large number of people worldwide. Recent studies proclaimed that risk factors can be substantially reduced by making it almost treatable, if detected at its early stages. This timely detection and classification demand an automated system, though procedure is quite complex. In this article, a novel strategy is adopted, which not only diagnoses the skin cancer but also assigns a proper class label. The proposed technique is principally built on saliency valuation and the selection of most discriminant deep features selection. The lesion contrast is being enhanced using proposed Gaussian method, followed by color space transformation from RGB to HSV. The new color space facilitates the saliency map construction process, utilizing inner and outer disjoint windows, by making the foreground and background maximally differentiable. From the segmented images, deep features are extracted by utilizing inception CNN model on two basic output layers. These extracted set of features are later fused using proposed decision-controlled parallel fusion method, prior to feature selection using proposed window distance-controlled entropy features selection method. The most discriminant features are later subjected to classification step. To demonstrate the efficiency of the proposed methods, three freely available datasets are utilized such as PH2, ISBI 2016, and ISBI 2017 with achieve accuracy is 97.74%, 96.1%, and 97%, respectively. Simulation results clearly reveal the improved performance of proposed method on all three datasets compared to existing methods. [ABSTRACT FROM AUTHOR]

Manual identification of brain tumors is an error-prone and tedious process for radiologists; therefore, it is crucial to adopt an automated system. The binary classification process, such as malignant or benign is relatively trivial; whereas, the multimodal brain tumors classification (T1, T2, T1CE, and Flair) is a challenging task for radiologists. Here, we present an automated multimodal classification method using deep learning for brain tumor type classification. The proposed method consists of five core steps. In the first step, the linear contrast stretching is employed using edge-based histogram equalization and discrete cosine transform (DCT). In the second step, deep learning feature extraction is performed. By utilizing transfer learning, two pre-trained convolutional neural network (CNN) models, namely VGG16 and VGG19, were used for feature extraction. In the third step, a correntropy-based joint learning approach was implemented along with the extreme learning machine (ELM) for the selection of best features. In the fourth step, the partial least square (PLS)-based robust covariant features were fused in one matrix. The combined matrix was fed to ELM for final classification. The proposed method was validated on the BraTS datasets and an accuracy of 97.8%, 96.9%, 92.5% for BraTs2015, BraTs2017, and BraTs2018, respectively, was achieved. [ABSTRACT FROM AUTHOR]

• Artificial bee colony based contrast stretching is perform. • Lesion detection through faster RCNN along with pixels information. • Deep features are extracted using entropy based activation function. • A Newton Raphson based most discriminate features are selected. Melanoma is the fatal form of skin cancer; however, its diagnosis at the primary stages significantly reduces the mortality rate. These days, the increasing numbers of skin cancer patients have boosted the requirement for a care decision support system - capable of detecting the lesions with high accuracy. In this work, a method is proposed for skin cancer localization and recognition by implementing a novel combination of a deep learning model and iteration-controlled Newton-Raphson (IcNR) based feature selection method. The proposed framework follows three primary steps - lesion localization through faster region based convolutional neural network (RCNN), deep feature extraction, and feature selection by IcNR approach. In the localization step, a new contrast stretching approach based on bee colony method (ABC) is being followed. The enhanced images along with their ground truths are later plugged into Fast-RCNN to get segmented images. A pre-trained model, DenseNet201, is utilized to extract deep features via transfer learning, which are later subjected to selection step using proposed IcNR approach. The selected most discriminant features are finally utilized for classification using multilayered feed forward neural networks. Tests are performed on ISBI2016 and ISBI2017 datasets to achieving an accuracy of 94.5% and 93.4%, respectively. Simulation results reveal that the proposed technique outperforms existing methods with greater accuracy, and time. [ABSTRACT FROM AUTHOR]