Skin Lesion Classification using Machine Learning Algorithms

Özkan, İlker Ali; Köklü, Murat

doi:10.18201/ijisae.2017534420

Cited by 99 publications

(41 citation statements)

References 17 publications

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“…There are a number of studies in the literature that employ machine learning techniques to classify melanocytic lesions into common nevus, atypical nevus and melanoma using the dermoscopic measures of the lesions mentioned above. The findings demonstrate that artificial neural networks perform better than support vector machines, K-nearest neighbor classifiers and also decision tree classifiers [10,11]. A feed-forward neural network having eighteen neurons in the hidden layer is illustrated to offer 92.5% accuracy when fed by the dermoscopic measures encoded using 1-of-N encoding scheme [11].…”

Section: Resultsmentioning

confidence: 96%

“…The findings demonstrate that artificial neural networks perform better than support vector machines, K-nearest neighbor classifiers and also decision tree classifiers [10,11]. A feed-forward neural network having eighteen neurons in the hidden layer is illustrated to offer 92.5% accuracy when fed by the dermoscopic measures encoded using 1-of-N encoding scheme [11]. On the other hand, a deep neural network with a Softmax activation function is reported to achieve 91.9% classification accuracy for the measures encoded using normalized scale encoding [10].…”

Section: Resultsmentioning

confidence: 97%

“…Diagnosis is made with respect to a community-recognized threshold value. The performances of the algorithms mentioned above are limited, and recently machine learning based algorithms have been gaining increased interest to improve classification of melanocytic lesions in dermoscopy [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neural Network Based Classification of Melanocytic Lesions in Dermoscopy: Role of Input Vector Encoding

Ertaş¹

2018

Electrica

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Melanocytic lesions are the main cause of death from skin cancer, and early diagnosis is the key to decreasing the mortality rate. This study assesses the role of input-vector encoding in neural network-based classification of melanocytic lesions in dermoscopy. Twelve dermoscopic measures from 200 melanocytic lesions are encoded by compact encoding, ACD encoding, 1-of-N encoding, normalized encoding, and raw encoding, resulting in five different input-vector sets. Feed-forward neural networks with one hidden layer and one output layer are designed with several neurons in the hidden layer, ranging from two to twenty-two for each type of input-vector set, to classify a melanocytic lesion into common nevus, atypical nevus, and melanoma. Accordingly, 105 networks are designed and trained using supervised learning and then tested by performing a 10-fold cross validation. All the neural networks achieve high sensitivities, specificities, and accuracies in classification. However, the network with seven neurons in the hidden layer and raw encoded dermoscopic measures as the input vector realizes the highest sensitivity (97.0%), specificity (98.1%), and accuracy (98.0%). The practical use of the network can facilitate lesion classification by retaining the needed expertise and minimizing diagnostic variability among dermatologists.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 97%

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Neural Network Based Classification of Melanocytic Lesions in Dermoscopy: Role of Input Vector Encoding

Ertaş¹

2018

Electrica

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“…The confusion matrix is provided an actual information estimated accuracy rate for classification during test procedures. A confusion matrix and parameters used to calculate the models' classification results using accuracy rate are shown in Table 1 [25][26][27]. Successful for 2-class classification performance measurement accuracy, sensitivity, specificity, accuracy, etc.…”

Section: Performance Measurementmentioning

confidence: 99%

Detection of Malaria Diseases with Residual Attention Network

Qanbar¹,

Tasdemir²

2019

ijisae

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To describe a model using classic machine learning techniques for creating machine learning systems, a person who specializes in this technique needs to extract feature vectors. This period also breaks into expert time. Also, these methods could not process raw data without preprocessing and expert assistance. Deep learning has made great progress in solving problems at this point, and machine learning research has continued for many years. Unlike traditional machine learning and image processing techniques, deep networks enable learning processes using raw data. In this study, a deep learning approach for the classification and diagnosis of malaria is developed. For this purpose, Residual Attention Network (RAN) a deep learning Convolutional Neural Network (CNN) technique was used with previously classified datasets. The goal is to design computer-aided software for classifying blood cell images (blood samples) as "parasitized" or "uninfected". In the program, a decision support system was implemented by a deep learning approach. As a result, the RAN model achieved the best ability to produce better results in processing and classification images compared to other algorithm types. RAN model's training simulation results showed a 95.79% classification accuracy rate. Using the Support Vector Machine (SVM) obtained only 83.30% classification accuracy rate. Besides, it is evaluated that for the classification of blood cell images and diagnosis of malaria using deep learning methods can be used successfully. In addition, deep learning methods have the advantage of automatically learning features from input data and require minimal input by specialists in automated malaria diagnosis.

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“…For example, an early study [19] used image segmentation and feature extraction to diagnose skin diseases. Another study [20] proposed an artificial neural network (ANN) based skin lesion classification model to classify the lesion into melanoma, abnormal, and typical classes. Moreover, [21] proposed a melanoma detection system using multiscale lesion-biased representation (MLR) and joint reverse classification (JRC).…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Federated Machine Learning-Based Intelligent System for Skin Disease Detection: A Step toward an Intelligent Dermoscopy Device

et al. 2021

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The prevalence of skin diseases has increased dramatically in recent decades, and they are now considered major chronic diseases globally. People suffer from a broad spectrum of skin diseases, whereas skin tumors are potentially aggressive and life-threatening. However, the severity of skin tumors can be managed (by treatment) if diagnosed early. Health practitioners usually apply manual or computer vision-based tools for skin tumor diagnosis, which may cause misinterpretation of the disease and lead to a longer analysis time. However, cutting-edge technologies such as deep learning using the federated machine learning approach have enabled health practitioners (dermatologists) in diagnosing the type and severity level of skin diseases. Therefore, this study proposes an adaptive federated machine learning-based skin disease model (using an adaptive ensemble convolutional neural network as the core classifier) in a step toward an intelligent dermoscopy device for dermatologists. The proposed federated machine learning-based architecture consists of intelligent local edges (dermoscopy) and a global point (server). The proposed architecture can diagnose the type of disease and continuously improve its accuracy. Experiments were carried out in a simulated environment using the International Skin Imaging Collaboration (ISIC) 2019 dataset (dermoscopy images) to test and validate the model’s classification accuracy and adaptability. In the future, this study may lead to the development of a federated machine learning-based (hardware) dermoscopy device to assist dermatologists in skin tumor diagnosis.

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Skin Lesion Classification using Machine Learning Algorithms

Cited by 99 publications

References 17 publications

Neural Network Based Classification of Melanocytic Lesions in Dermoscopy: Role of Input Vector Encoding

Neural Network Based Classification of Melanocytic Lesions in Dermoscopy: Role of Input Vector Encoding

Detection of Malaria Diseases with Residual Attention Network

An Adaptive Federated Machine Learning-Based Intelligent System for Skin Disease Detection: A Step toward an Intelligent Dermoscopy Device

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