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

Yıl 2018, Cilt: 18 Sayı: 2, 242 - 248, 03.08.2018

Gökhan Ertaş

Öz

DOI: 10.26650/electrica.2018.97856

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, adrenocortical dysplasia (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.

Anahtar Kelimeler

Dermoscopy, melanocytic lesion, multilayer perceptron, encoding, classification, classification

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

Öz

DOI: 10.26650/electrica.2018.97856

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, adrenocortical dysplasia (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.

Anahtar Kelimeler

Dermoscopy, melanocytic lesion, multilayer perceptron, encoding, classification

Kaynakça

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