An integrated framework of skin lesion detection and recognition through saliency method and optimal deep neural network
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RECENT ADVANCES IN DEEP LEARNING FOR MEDICAL IMAGE PROCESSING
An integrated framework of skin lesion detection and recognition through saliency method and optimal deep neural network features selection M. Attique Khan1,3 • Tallha Akram2 • Muhammad Sharif3 Syed Ahmad Chan Bukhari5
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Kashif Javed4 • Muhammad Rashid3
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Received: 17 December 2018 / Accepted: 9 October 2019 Springer-Verlag London Ltd., part of Springer Nature 2019
Abstract 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. Keywords Melanoma Saliency segmentation CNN features Fusion Optimal features Neural network
1 Introduction & Muhammad Sharif [email protected] Syed Ahmad Chan Bukhari [email protected] 1
Department of Computer Science and Engineering, HITEC University, Museum Road, Taxila, Pakistan
2
Department of ECE, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan
3
Department of CS, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan
4
Department of Robotics, SMME NUST, Islamabad, Pakistan
5
Division of Computer Science, Mathematics and Science, College of Professional Studies, St. John’s University, New York, USA
Malignant melanoma, being a minority of skin cancers but most deadly, develops from melanocytes cells. The occurrence of melanoma has increased drastically in the last decade, especially among males [1, 2]. In the USA only, an estima
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