Very deep convolutional networks for skin lesion classification

Other Title(s)

الشبكة العصبية الملتفّة العميقة جدا لتصنيف الأورام الجلدية

Author

al-Nuwami, Majdi Rashid Salim

Source

Journal of King Abdulaziz University : Engineering Sciences

Issue

Vol. 30, Issue 2 (31 Dec. 2019), pp.43-54, 12 p.

Publisher

King Abdulaziz University Scientific Publishing Center

Publication Date

2019-12-31

Country of Publication

Saudi Arabia

No. of Pages

12

Main Subjects

Educational Sciences

Abstract EN

medical imaging and diagnostic radiology, is fast becoming a key tool in Computer-Aided Systems.

A visual examination of a skin lesion is one of many potential applications of Computer-Aided Diagnosis (CAD).

In this paper, a classification algorithm was developed and trained based on Densely Connected Convolutional Networks (DenseNets) for skin lesion classification.

This work combines the use of dermatoscopic images pre-processing and deep convolutional network.

Experimental studies were conducted using more than 30,000 dermatoscopic images from multiple open- access archives.

Initially, the paper examined the impact of proposed image pre-processing and tuning on the accuracy of a binary classification CNN.

The result showed that the binary classifier achieves a higher area under the curve (AUC), 0.93, when trained on processed data compared to 0.85 before.

In the second stage, two different data-sets were created a 3-ary classes and 9-ary classes.

The validation results showed that the proposed work achieved validation accuracy of 81.2 ± 1.1% in the 3-ary and of 60.1 ± 1.3%.

In the 9-ary classification studies.

The proposed combination of dermatoscopic images pre-processing and deeper convolutional network can achieve better performance by learning more complex features of the input data with a more efficient memory implementation

American Psychological Association (APA)

al-Nuwami, Majdi Rashid Salim. 2019. Very deep convolutional networks for skin lesion classification. Journal of King Abdulaziz University : Engineering Sciences،Vol. 30, no. 2, pp.43-54.
https://search.emarefa.net/detail/BIM-1243780

Modern Language Association (MLA)

al-Nuwami, Majdi Rashid Salim. Very deep convolutional networks for skin lesion classification. Journal of King Abdulaziz University : Engineering Sciences Vol. 30, no. 2 (2019), pp.43-54.
https://search.emarefa.net/detail/BIM-1243780

American Medical Association (AMA)

al-Nuwami, Majdi Rashid Salim. Very deep convolutional networks for skin lesion classification. Journal of King Abdulaziz University : Engineering Sciences. 2019. Vol. 30, no. 2, pp.43-54.
https://search.emarefa.net/detail/BIM-1243780

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references : p. 52-53

Record ID

BIM-1243780