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Development of A Clinically-Oriented Expert System for Differentiating Melanocytic from Non-melanocytic Skin Lesions

Qaisar Abbas
2017 International Journal of Advanced Computer Science and Applications  
Afterward, these features are further purified through stack-based autoencoders (SAE) and classified by a softmax linear classifier into categories of melanocytic and non-melanocytic skin lesions.  ...  For the development of COE-Deep system, the convolutional neural network (CNN) model is employed to extract the prominent features from region-ofinterest (ROI) skin images.  ...  Similarly, the authors in [14] used CNN model to dermoscopy images to classify malignant melanoma skin lesions.  ... 
doi:10.14569/ijacsa.2017.080704 fatcat:ubtnl4mxsbbcbkonjhwhq7q7ne

What evidence does deep learning model use to classify Skin Lesions? [article]

Xiaoxiao Li, Junyan Wu, Eric Z. Chen, Hongda Jiang
2019 arXiv   pre-print
In this paper, we propose a method to interpret the deep learning classification findings. Firstly, we propose an accurate neural network architecture to classify skin lesions.  ...  However, accurately classifying skin lesions by eye, especially in the early stage of melanoma, is extremely challenging for the dermatologists.  ...  Deep learning feature interpretation In order to interpret the features the deep learning classifier used to classify skin lesions, we did the prediction difference analysis as described in section II-B  ... 
arXiv:1811.01051v3 fatcat:rg2lhdxo2ngi7gtmjpevc7q6wu

Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures

Jwan Saeed, Subhi Zeebaree
2021 Journal of Applied Science and Technology Trends  
Skin cancer is among the primary cancer types that manifest due to various dermatological disorders, which may be further classified into several types based on morphological features, color, structure  ...  Attempts to overcome these challenges have been made by analyzing the images using deep learning neural networks to perform skin cancer detection.  ...  They are classifying skin lesions involving augmenting labeled images, extracting features, and predicting skin lesions.  ... 
doi:10.38094/jastt20189 fatcat:f7nansjvyrfxfmr27uzvp22oqi

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Mohamed A. Kassem, Khalid M. Hosny, Robertas Damaševičius, Mohamed Meselhy Eltoukhy
2021 Diagnostics  
Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems.  ...  The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.  ...  [165] utilized deep learning and conventional image processing to extract different skin lesion features.  ... 
doi:10.3390/diagnostics11081390 fatcat:r4gyqfwberfofhcbx2xsn7vpf4

Deep Learning-Based System for Automatic Melanoma Detection

Adekanmi A. Adegun, Serestina Viriri
2019 IEEE Access  
They have been limited in performance due to the complex visual characteristics of the skin lesion images which consists of inhomogeneous features and fuzzy boundaries.  ...  INDEX TERMS Deep learning-based, encoding-decoding network, pixel-wise classification, melanoma, skin lesion, segmentation.  ...  skin lesions images; 3) LESION-CLASSIFIER We devise a new predictive method called Lesion-classifier which is computationally efficient to classify skin lesions into melanoma and non-melanoma in a pixel-wise  ... 
doi:10.1109/access.2019.2962812 fatcat:pdhoaiyphjc2hnol4yubdrpsze

Melanoma Segmentation and Classification using Deep Learning

Initially deep learning based U-Net algorithm is used to segment the lesion region from the nearby healthy skin and then extract discriminate features with the help of Convolutional Neural Network.  ...  VGG16 Net algorithm is used to classify every lesion in a dermoscopic image as a Benign or Melanoma. Results are found from classification with and without segmented images.  ...  Then classify melanoma using SVM classifier with accuracy 82.2% was presented by [4] . Linear classifier to classify 10 different skin lesions was proposed by [12] .  ... 
doi:10.35940/ijitee.l2516.1081219 fatcat:gjmzpumgt5dzjml7xqitlk7xuq

Skin Lesion Classification Using Hybrid Deep Neural Networks [article]

Amirreza Mahbod, Gerald Schaefer, Chunliang Wang, Rupert Ecker, Isabella Ellinger
2019 arXiv   pre-print
We use three pre-trained deep models, namely AlexNet, VGG16 and ResNet-18, as deep feature generators. The extracted features then are used to train support vector machine classifiers.  ...  Accurately diagnosing skin lesions to discriminate between benign and malignant skin lesions is crucial to ensure appropriate patient treatment.  ...  A number of works [11, 12, 13, 10] have tried to extract deep features from skin lesion images and then train a classical classifier.  ... 
arXiv:1702.08434v2 fatcat:5g4ojh4qhfglrjlsvlpfb53vvm

A Novel Approach for Detection of Skin Cancer using Back Propagation Neural Network Jignyasa Sanghavi

Jignyasa Sanghavi
2019 Helix  
Considering the seriousness of this domain, we tried to develop a system which will automatically detect the Melanoma skin cancer from skin lesion images.  ...  The non melanoma skin cancer are reported occurring due to the exposure of skin to harmful ultra-violet (UV) rays, the rays internally disturbs the skin cell structure, causing malfunctioning of the skin  ...  Acknowledgement I am thankful to International Skin Imaging Collaboration for providing the skin cancer dataset at ISIC platform.  ... 
doi:10.29042/2019-5847-5851 fatcat:ao64p6fp35g5zoumkwae3usap4

Skin Cancer Detection: A Review Using Deep Learning Techniques

Mehwish Dildar, Shumaila Akram, Muhammad Irfan, Hikmat Ullah Khan, Muhammad Ramzan, Abdur Rehman Mahmood, Soliman Ayed Alsaiari, Abdul Hakeem M Saeed, Mohammed Olaythah Alraddadi, Mater Hussen Mahnashi
2021 International Journal of Environmental Research and Public Health  
Lesion parameters such as symmetry, color, size, shape, etc. are used to detect skin cancer and to distinguish benign skin cancer from melanoma.  ...  This paper presents a detailed systematic review of deep learning techniques for the early detection of skin cancer.  ...  [34] proposed a technique to extract deep features from various wellestablished and pre-trained deep CNNs for skin lesions classification.  ... 
doi:10.3390/ijerph18105479 pmid:34065430 pmcid:PMC8160886 fatcat:amoeo6q5jnemfdpgkryryh6o3m

Deep Semantic Segmentation and Multi-Class Skin Lesion Classification based on Convolutional Neural Network

Muhammad Almas Anjum, Javaria Amin, Muhammad Sharif, Habib Ullah Khan, Sheraz Arshad Malik, Seifedine Kadry
2020 IEEE Access  
The proposed method accurately localized, segmented and classified the skin lesion at an early stage.  ...  Later in Phase III, extract deep features using ResNet-18 model and optimized features are selected using ant colony optimization (ACO) method.  ...  In future this work, further enhance to apply the re-enforcement learning for accurately classify the skin lesion. Figure1.  ... 
doi:10.1109/access.2020.3009276 fatcat:eb6t3n5rc5adpj4cjxntezbkne

Skin Lesions Classification Using Deep Learning Techniques: Review

Omar Sedqi Kareem, Adnan Mohsin Abdulazee, Diyar Qader Zeebaree
2021 Asian Journal of Research in Computer Science  
Advances in technology and growth in computational capabilities have allowed machine learning and deep learning algorithms to analyze skin lesion images.  ...  The result of classifications and segmentation from the skin lesion images will be processed better using the ensemble deep learning algorithm.  ...  It consists mainly of several convolutional layers of the two-stages FCRN approach that use deep residual networks to segment and classify skin lesions.  ... 
doi:10.9734/ajrcos/2021/v9i130210 fatcat:zjpitxunafeg3kg5cjauxjsgki

Artificial Intelligence in Cutaneous Oncology

Yu Seong Chu, Hong Gi An, Byung Ho Oh, Sejung Yang
2020 Frontiers in Medicine  
In addition, the universal use of dermoscopy, which allows for non-invasive inspection of the upper dermal level of skin lesions with a usual 10-fold magnification, adds to the image storage and analysis  ...  Skin cancer, previously known to be a common disease in Western countries, is becoming more common in Asian countries. Skin cancer differs from other carcinomas in that it is visible to our eyes.  ...  This enables deep learning to be applied to IoT devices for faster performances than large networks, which will lead to more active research into skin lesion detection using applications.  ... 
doi:10.3389/fmed.2020.00318 pmid:32754606 pmcid:PMC7366843 fatcat:557xllh2dzf5hbnrrmcdbg2kyi

Robust Skin lesion Classification via Machine Intelligence

2020 International journal of recent technology and engineering  
Skin cancer is typically growth and spread of cells or lesion on the uppermost part or layer of skin known as the epidermis.  ...  Dermatologists use dermatoscopic images to identify the type of skin cancer by identification of asymmetry, border, colour, texture & size mole or a lesion.  ...  [2] Used deep CNN to train the model and then classifying it via SVM to detection of skin cancer using color and texture features. Soniya et al.  ... 
doi:10.35940/ijrte.f9519.038620 fatcat:ul2w44wybzfethstwgthpc65qy

Skin Cancer Diagnostic using Machine Learning Techniques - Shearlet Transform and Naïve Bayes Classifier

2019 International Journal of Engineering and Advanced Technology  
The selected subband coefficients are directly applied to the naïve Bayes classifier. Performance of skin cancer classification system is measured in terms of accuracy.  ...  Results show that a better classification accuracy of 90.5 % is achieved at 3rd level with 100 coefficients of shearlet transform and naïve Bayes classifier for skin image classification system.  ...  Skin lesion classification using deep ensemble learning is described in [5] . Neural network architecture is used for feature extraction.  ... 
doi:10.35940/ijeat.b4916.129219 fatcat:ta562de62fg4fekonhreh4br2q

Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine

Farhat Afza, Muhammad Sharif, Muhammad Attique Khan, Usman Tariq, Hwan-Seung Yong, Jaehyuk Cha
2022 Sensors  
In this paper, a new method for multiclass skin lesion classification using best deep learning feature fusion and an extreme learning machine is proposed.  ...  Manually detecting skin lesions from dermoscopy images is a difficult and time-consuming process.  ...  The utilization of deep features for skin lesion detection and classification has been shown to be of immense importance in the last few years compared to the traditional feature extraction techniques  ... 
doi:10.3390/s22030799 pmid:35161553 pmcid:PMC8838278 fatcat:h7yoykfsvfgqtmb4jy4l4pp7zy
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