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

Gökhan Ertaş

Article Details

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

Authors:

Gökhan Ertaş

Publisher:

İstanbul University-Cerrahpasa

Main Topics:

Applied physics

Journal:

Electrica 2018 Vol.18 Issue 2, pp.242-248

Document Type:

Article

Publication Date:

8/2018

Document Language:

English

Document ID (Org No.):

2000777622

Frequency:

Bi-Annual

eISSN:

2619-9831

Keywords:

Abstract:

 




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.


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Article Details

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

Authors:

Gökhan Ertaş

Publisher:

İstanbul University-Cerrahpasa

Main Topics:

Applied physics

Journal:

Electrica 2018 Vol.18 Issue 2, pp.242-248

Document Type:

Article

Publication Date:

8/2018

Document Language:

English

Document ID (Org No.):

2000777622

Frequency:

Bi-Annual

eISSN:

2619-9831

Dermoscopy

melanocytic lesion

multilayer perceptron

encoding

classification

Issues

Article Details

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

Authors: Gökhan Ertaş

Publisher: İstanbul University-Cerrahpasa

Main Topics: Applied physics

Journal: Electrica 2018 Vol.18 Issue 2, pp.242-248

Document Type: Article

Publication Date: 8/2018

Document Language: English

Document ID (Org No.): 2000777622

Frequency: Bi-Annual

eISSN: 2619-9831

Dermoscopy

melanocytic lesion

multilayer perceptron

encoding

classification

Issues

Authors:

Gökhan Ertaş

Publisher:

İstanbul University-Cerrahpasa

Main Topics:

Applied physics

Journal:

Electrica 2018 Vol.18 Issue 2, pp.242-248

Document Type:

Article

Publication Date:

8/2018

Document Language:

English

Document ID (Org No.):

2000777622

Frequency:

Bi-Annual

eISSN:

2619-9831