SKIN CANCER LESION CLASSIFICATION USING LBP BASED HYBRID CLASSIFIER
Main Article Content
Abstract
Skin cancer is a most dangerous type of cancer found in humans. It is found in various types such as melanoma, basal cell carcinoma and squamous cell carcinoma. Among others, Melanoma is the most serious and dangerous cancer which leads from a simple skin mark to a tumour. Early detection of the type of skin cancer can helps in better cure. In this paper, a new method is proposed for the detection of type of skin cancer. The proposed method integrates the features of DRLBP (Dominant rotated local binary pattern) and MRELBP (median robust extended local binary pattern) methods for feature extraction of the skin cancer lesions. The support vector machine (SVM) classifier is then used to classify the images based on the calculated features. To evaluate the performance of the proposed method, skin cancer images containing 367 lesions are used from ISIC standard dataset from the results, it is analysed that the proposed method can extracts microstructure and macrostructure texture information. Furthermore, it is robust to the rotation variation. It is also observed that the proposed method gives better results than the other state of the art local binary pattern based feature extraction methods.
Downloads
Download data is not yet available.
Article Details
Section
Articles
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.