An integrated framework of skin lesion detection and recognition through saliency method and optimal deep neural network features selection

  • M. Attique Khan
  • Tallha Akram
  • Muhammad SharifEmail author
  • Kashif Javed
  • Muhammad Rashid
  • Syed Ahmad Chan Bukhari
Recent Advances in Deep Learning for Medical Image Processing


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.


Melanoma Saliency segmentation CNN features Fusion Optimal features Neural network 


Compliance with ethical standards

Conflict of interest

All authors have no conflict of interest and contribute equally in this work for results compilation and other technical support.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringHITEC UniversityTaxilaPakistan
  2. 2.Department of ECECOMSATS University IslamabadIslamabadPakistan
  3. 3.Department of CSCOMSATS University IslamabadIslamabadPakistan
  4. 4.Department of RoboticsSMME NUSTIslamabadPakistan
  5. 5.Division of Computer Science, Mathematics and Science, College of Professional StudiesSt. John’s UniversityNew YorkUSA

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