Automatic skin lesions detection from images through microscopic hybrid features set and machine learning classifiers

Skin cancer occurrences increase exponentially worldwide due to the lack of awareness of significant populations and skin specialists. Medical imaging can help with early detection and more accurate diagnosis of skin cancer. The physicians usually follow the manual diagnosis method in their clinics but nonprofessional dermatologists sometimes affect the accuracy of the results. Thus, the automated system is required to assist physicians in diagnosing skin cancer at early stage precisely to decrease the mortality rate. This article presents an automatic skin lesions detection through a microscopic hybrid feature set and machine learning‐based classification. The employment of deep features through AlexNet architecture with local optimal‐oriented pattern can accurately predict skin lesions. The proposed model is tested on two open‐access datasets PAD‐UFES‐20 and MED‐NODE comprising melanoma and nevus images. Experimental results on both datasets exhibit the efficacy of hybrid features with the help of machine learning. Finally, the proposed model achieved 94.7% accuracy using an ensemble classifier.

[1]  Tanzila Saba,et al.  Skin lesion segmentation and classification: A unified framework of deep neural network features fusion and selection , 2019, Expert Syst. J. Knowl. Eng..

[2]  T. Saba,et al.  IoMT Enabled Melanoma Detection Using Improved Region Growing Lesion Boundary Extraction , 2022, Computers, Materials & Continua.

[3]  Hafiz Tayyab Rauf,et al.  A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion , 2022, Computers, Materials & Continua.

[4]  T. Saba,et al.  Skin cancer detection from dermoscopic images using deep learning and fuzzy k‐means clustering , 2021, Microscopy research and technique.

[5]  L. Moraru,et al.  Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks , 2021, Diagnostics.

[6]  Md. Sipon Miah,et al.  An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models , 2021 .

[7]  Amjad Rehman,et al.  Statistical Histogram Decision Based Contrast Categorization of Skin Lesion Datasets Dermoscopic Images , 2021 .

[8]  Muhammad Attique Khan,et al.  Prediction of COVID-19 - Pneumonia based on Selected Deep Features and One Class Kernel Extreme Learning Machine , 2020, Computers & Electrical Engineering.

[9]  Robertas Damaševičius,et al.  A framework of human action recognition using length control features fusion and weighted entropy-variances based feature selection , 2020, Image Vis. Comput..

[10]  Ayyaz Hussain,et al.  A Review on Multi-Organs Cancer Detection using Advanced Machine Learning Techniques. , 2020, Current medical imaging.

[11]  Aysegül Uçar,et al.  Skin lesion segmentation using fully convolutional networks: A comparative experimental study , 2020, Expert Syst. Appl..

[12]  Hieu X. Le,et al.  Transfer learning with class-weighted and focal loss function for automatic skin cancer classification , 2020, ArXiv.

[13]  Thar Baker,et al.  SaS-BCI: a new strategy to predict image memorability and use mental imagery as a brain-based biometric authentication , 2020, Neural Computing and Applications.

[14]  André G. C. Pacheco,et al.  PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones , 2020, Data in brief.

[15]  Amjad Rehman,et al.  A Sustainable Deep Learning Framework for Object Recognition Using Multi-Layers Deep Features Fusion and Selection , 2020, Sustainability.

[16]  T. Saba,et al.  A comprehensive study of mobile-health based assistive technology for the healthcare of dementia and Alzheimer’s disease (AD) , 2019, Health Care Management Science.

[17]  Tanzila Saba,et al.  A comparative study of features selection for skin lesion detection from dermoscopic images , 2019, Network Modeling Analysis in Health Informatics and Bioinformatics.

[18]  Tanzila Saba,et al.  Brain tumor detection: a long short-term memory (LSTM)-based learning model , 2019, Neural Computing and Applications.

[19]  Muhammad Sharif,et al.  Microscopic skin laceration segmentation and classification: A framework of statistical normal distribution and optimal feature selection , 2019, Microscopy research and technique.

[20]  Tanzila Saba,et al.  Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction , 2019, Journal of Medical Systems.

[21]  Eric Z. Chen,et al.  From Deep Learning Towards Finding Skin Lesion Biomarkers , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[22]  Zahid Ullah,et al.  Performance Analysis of Low-Level and High-Level Intuitive Features for Melanoma Detection , 2019, Electronics.

[23]  Muhammad Sharif,et al.  Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion , 2019, Microscopy research and technique.

[24]  Tanzila Saba,et al.  Brain Tumor Detection from MRI images using Multi-level Wavelets , 2019, 2019 International Conference on Computer and Information Sciences (ICCIS).

[25]  Muhammad Younus Javed,et al.  Multi-Model Deep Neural Network based Features Extraction and Optimal Selection Approach for Skin Lesion Classification , 2019, 2019 International Conference on Computer and Information Sciences (ICCIS).

[26]  Ayyaz Hussain,et al.  Fuzzy C-means and region growing based classification of tumor from mammograms using hybrid texture feature , 2018, J. Comput. Sci..

[27]  Mudassar Raza,et al.  Fundus image classification methods for the detection of glaucoma: A review , 2018, Microscopy research and technique.

[28]  Zahid Mehmood,et al.  Scene analysis and search using local features and support vector machine for effective content-based image retrieval , 2018, Artificial Intelligence Review.

[29]  Paul L. Rosin,et al.  Clinical Skin Lesion Diagnosis Using Representations Inspired by Dermatologist Criteria , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[30]  Tanzila Saba,et al.  An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach , 2018, Microscopy research and technique.

[31]  Zahid Mehmood,et al.  A Novel Technique Based on Visual Words Fusion Analysis of Sparse Features for Effective Content-Based Image Retrieval , 2018 .

[32]  LinLin Shen,et al.  Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network , 2017, Sensors.

[33]  Umapada Pal,et al.  LOOP Descriptor: Encoding Repeated Local Patterns for Fine-grained Visual Identification of Lepidoptera , 2017, ArXiv.

[34]  Tanzila Saba,et al.  Retinal imaging analysis based on vessel detection , 2017, Microscopy research and technique.

[35]  D. Sharada Mani,et al.  Face recognition based on kirsch compass kernel operator , 2017, 2017 International Conference on Communication and Signal Processing (ICCSP).

[36]  Amjad Rehman,et al.  An evolution based hybrid approach for heart diseases classification and associated risk factors identification , 2017 .

[37]  Amjad Rehman,et al.  3D bones segmentation based on CT images visualization , 2017 .

[38]  Marcel F. Jonkman,et al.  MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images , 2015, Expert Syst. Appl..

[39]  John R. Smith,et al.  Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images , 2015, MLMI.

[40]  Mznah Al-Rodhaan,et al.  Intelligent fuzzy approach for fast fractal image compression , 2014, EURASIP J. Adv. Signal Process..

[41]  Amjad Rehman,et al.  Fuzzy Phoneme Classification Using Multi-speaker Vocal Tract Length Normalization , 2014 .

[42]  Alina A. von Davier,et al.  Cross-Validation , 2014 .

[43]  T. Asfour,et al.  Facts & Figures , 1962, Contemporary Canadian Picture Books.