Loading…
Skin lesion classification using modified deep and multi-directional invariant handcrafted features
Skin lesions encompass various skin conditions, including cancerous growths resulting from uncontrolled proliferation of skin cells. Globally, this disease affects a significant portion of the population, with millions of fatalities recorded. Over the past three decades, there has been a concerning...
Saved in:
Published in: | Journal of network and computer applications 2024-11, Vol.231, p.103949, Article 103949 |
---|---|
Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | cdi_FETCH-LOGICAL-c181t-5f8161340fcd70266648ac998ccb26d25b358b473e6306f98582e267bf1ababb3 |
container_end_page | |
container_issue | |
container_start_page | 103949 |
container_title | Journal of network and computer applications |
container_volume | 231 |
creator | Pradhan, Jitesh Singh, Ashish Kumar, Abhinav Khan, Muhammad Khurram |
description | Skin lesions encompass various skin conditions, including cancerous growths resulting from uncontrolled proliferation of skin cells. Globally, this disease affects a significant portion of the population, with millions of fatalities recorded. Over the past three decades, there has been a concerning escalation in diagnosed cases of skin cancer. Early detection is crucial for effective treatment, as late diagnosis significantly heightens mortality risk. Existing research often focuses on either handcrafted or deep features, neglecting the diverse textural and structural properties inherent in skin lesion images. Additionally, reliance on a single optimizer in CNN-based schemes poses efficiency challenges. To tackle these issues, this paper presents two novel approaches for classifying skin lesions in dermoscopic images to assess cancer severity. The first approach enhances classification accuracy by leveraging a modified VGG-16 network and employing both RMSProp and Adam optimizers. The second approach introduces a Hybrid CNN Model, integrating deep features from the modified VGG-16 network with handcrafted color and multi-directional texture features. Color features are extracted using a non-uniform cumulative probability-based histogram method, while texture features are derived from a 45∘ rotated complex wavelet filter-based dual-tree complex wavelet transform. The amalgamated features facilitate accurate prediction of skin lesion classes. Evaluation on ISIC 2017 skin cancer classification challenge images demonstrates significant performance enhancements over existing techniques. |
doi_str_mv | 10.1016/j.jnca.2024.103949 |
format | article |
fullrecord | <record><control><sourceid>elsevier_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1016_j_jnca_2024_103949</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S1084804524001267</els_id><sourcerecordid>S1084804524001267</sourcerecordid><originalsourceid>FETCH-LOGICAL-c181t-5f8161340fcd70266648ac998ccb26d25b358b473e6306f98582e267bf1ababb3</originalsourceid><addsrcrecordid>eNp9kMtOwzAQRb0AiVL4AVb-gRS_4joSG1TxkiqxANaWH2NwSJ3KTirx9yQKa1ajGd0zmjkI3VCyoYTK23bTJmc2jDAxDXgjmjO0okSJShFRX6DLUlpCiBQNXyH39h0T7qDEPmHXmVJiiM4MczuWmD7xoffTCDz2AEdskseHsRti5WMGN-dMh2M6mRxNGvDXFHDZhGECAphhzFCu0HkwXYHrv7pGH48P77vnav_69LK731eOKjpUdVBUUi5IcH5LmJRSKOOaRjlnmfSstrxWVmw5SE5kaFStGDC5tYEaa6zla8SWvS73pWQI-pjjweQfTYme1ehWz2r0rEYvaiboboFguuwUIeviIiQHy3_a9_E__BdxFHDm</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Skin lesion classification using modified deep and multi-directional invariant handcrafted features</title><source>Elsevier</source><creator>Pradhan, Jitesh ; Singh, Ashish ; Kumar, Abhinav ; Khan, Muhammad Khurram</creator><creatorcontrib>Pradhan, Jitesh ; Singh, Ashish ; Kumar, Abhinav ; Khan, Muhammad Khurram</creatorcontrib><description>Skin lesions encompass various skin conditions, including cancerous growths resulting from uncontrolled proliferation of skin cells. Globally, this disease affects a significant portion of the population, with millions of fatalities recorded. Over the past three decades, there has been a concerning escalation in diagnosed cases of skin cancer. Early detection is crucial for effective treatment, as late diagnosis significantly heightens mortality risk. Existing research often focuses on either handcrafted or deep features, neglecting the diverse textural and structural properties inherent in skin lesion images. Additionally, reliance on a single optimizer in CNN-based schemes poses efficiency challenges. To tackle these issues, this paper presents two novel approaches for classifying skin lesions in dermoscopic images to assess cancer severity. The first approach enhances classification accuracy by leveraging a modified VGG-16 network and employing both RMSProp and Adam optimizers. The second approach introduces a Hybrid CNN Model, integrating deep features from the modified VGG-16 network with handcrafted color and multi-directional texture features. Color features are extracted using a non-uniform cumulative probability-based histogram method, while texture features are derived from a 45∘ rotated complex wavelet filter-based dual-tree complex wavelet transform. The amalgamated features facilitate accurate prediction of skin lesion classes. Evaluation on ISIC 2017 skin cancer classification challenge images demonstrates significant performance enhancements over existing techniques.</description><identifier>ISSN: 1084-8045</identifier><identifier>DOI: 10.1016/j.jnca.2024.103949</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Complex wavelet transform ; Convolutional neural network ; Deep features ; Invariant features ; Probability-histogram ; Skin lesion</subject><ispartof>Journal of network and computer applications, 2024-11, Vol.231, p.103949, Article 103949</ispartof><rights>2024 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c181t-5f8161340fcd70266648ac998ccb26d25b358b473e6306f98582e267bf1ababb3</cites><orcidid>0000-0001-9367-7069</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Pradhan, Jitesh</creatorcontrib><creatorcontrib>Singh, Ashish</creatorcontrib><creatorcontrib>Kumar, Abhinav</creatorcontrib><creatorcontrib>Khan, Muhammad Khurram</creatorcontrib><title>Skin lesion classification using modified deep and multi-directional invariant handcrafted features</title><title>Journal of network and computer applications</title><description>Skin lesions encompass various skin conditions, including cancerous growths resulting from uncontrolled proliferation of skin cells. Globally, this disease affects a significant portion of the population, with millions of fatalities recorded. Over the past three decades, there has been a concerning escalation in diagnosed cases of skin cancer. Early detection is crucial for effective treatment, as late diagnosis significantly heightens mortality risk. Existing research often focuses on either handcrafted or deep features, neglecting the diverse textural and structural properties inherent in skin lesion images. Additionally, reliance on a single optimizer in CNN-based schemes poses efficiency challenges. To tackle these issues, this paper presents two novel approaches for classifying skin lesions in dermoscopic images to assess cancer severity. The first approach enhances classification accuracy by leveraging a modified VGG-16 network and employing both RMSProp and Adam optimizers. The second approach introduces a Hybrid CNN Model, integrating deep features from the modified VGG-16 network with handcrafted color and multi-directional texture features. Color features are extracted using a non-uniform cumulative probability-based histogram method, while texture features are derived from a 45∘ rotated complex wavelet filter-based dual-tree complex wavelet transform. The amalgamated features facilitate accurate prediction of skin lesion classes. Evaluation on ISIC 2017 skin cancer classification challenge images demonstrates significant performance enhancements over existing techniques.</description><subject>Complex wavelet transform</subject><subject>Convolutional neural network</subject><subject>Deep features</subject><subject>Invariant features</subject><subject>Probability-histogram</subject><subject>Skin lesion</subject><issn>1084-8045</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kMtOwzAQRb0AiVL4AVb-gRS_4joSG1TxkiqxANaWH2NwSJ3KTirx9yQKa1ajGd0zmjkI3VCyoYTK23bTJmc2jDAxDXgjmjO0okSJShFRX6DLUlpCiBQNXyH39h0T7qDEPmHXmVJiiM4MczuWmD7xoffTCDz2AEdskseHsRti5WMGN-dMh2M6mRxNGvDXFHDZhGECAphhzFCu0HkwXYHrv7pGH48P77vnav_69LK731eOKjpUdVBUUi5IcH5LmJRSKOOaRjlnmfSstrxWVmw5SE5kaFStGDC5tYEaa6zla8SWvS73pWQI-pjjweQfTYme1ehWz2r0rEYvaiboboFguuwUIeviIiQHy3_a9_E__BdxFHDm</recordid><startdate>202411</startdate><enddate>202411</enddate><creator>Pradhan, Jitesh</creator><creator>Singh, Ashish</creator><creator>Kumar, Abhinav</creator><creator>Khan, Muhammad Khurram</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0001-9367-7069</orcidid></search><sort><creationdate>202411</creationdate><title>Skin lesion classification using modified deep and multi-directional invariant handcrafted features</title><author>Pradhan, Jitesh ; Singh, Ashish ; Kumar, Abhinav ; Khan, Muhammad Khurram</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c181t-5f8161340fcd70266648ac998ccb26d25b358b473e6306f98582e267bf1ababb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Complex wavelet transform</topic><topic>Convolutional neural network</topic><topic>Deep features</topic><topic>Invariant features</topic><topic>Probability-histogram</topic><topic>Skin lesion</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pradhan, Jitesh</creatorcontrib><creatorcontrib>Singh, Ashish</creatorcontrib><creatorcontrib>Kumar, Abhinav</creatorcontrib><creatorcontrib>Khan, Muhammad Khurram</creatorcontrib><collection>CrossRef</collection><jtitle>Journal of network and computer applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pradhan, Jitesh</au><au>Singh, Ashish</au><au>Kumar, Abhinav</au><au>Khan, Muhammad Khurram</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Skin lesion classification using modified deep and multi-directional invariant handcrafted features</atitle><jtitle>Journal of network and computer applications</jtitle><date>2024-11</date><risdate>2024</risdate><volume>231</volume><spage>103949</spage><pages>103949-</pages><artnum>103949</artnum><issn>1084-8045</issn><abstract>Skin lesions encompass various skin conditions, including cancerous growths resulting from uncontrolled proliferation of skin cells. Globally, this disease affects a significant portion of the population, with millions of fatalities recorded. Over the past three decades, there has been a concerning escalation in diagnosed cases of skin cancer. Early detection is crucial for effective treatment, as late diagnosis significantly heightens mortality risk. Existing research often focuses on either handcrafted or deep features, neglecting the diverse textural and structural properties inherent in skin lesion images. Additionally, reliance on a single optimizer in CNN-based schemes poses efficiency challenges. To tackle these issues, this paper presents two novel approaches for classifying skin lesions in dermoscopic images to assess cancer severity. The first approach enhances classification accuracy by leveraging a modified VGG-16 network and employing both RMSProp and Adam optimizers. The second approach introduces a Hybrid CNN Model, integrating deep features from the modified VGG-16 network with handcrafted color and multi-directional texture features. Color features are extracted using a non-uniform cumulative probability-based histogram method, while texture features are derived from a 45∘ rotated complex wavelet filter-based dual-tree complex wavelet transform. The amalgamated features facilitate accurate prediction of skin lesion classes. Evaluation on ISIC 2017 skin cancer classification challenge images demonstrates significant performance enhancements over existing techniques.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.jnca.2024.103949</doi><orcidid>https://orcid.org/0000-0001-9367-7069</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1084-8045 |
ispartof | Journal of network and computer applications, 2024-11, Vol.231, p.103949, Article 103949 |
issn | 1084-8045 |
language | eng |
recordid | cdi_crossref_primary_10_1016_j_jnca_2024_103949 |
source | Elsevier |
subjects | Complex wavelet transform Convolutional neural network Deep features Invariant features Probability-histogram Skin lesion |
title | Skin lesion classification using modified deep and multi-directional invariant handcrafted features |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T06%3A51%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-elsevier_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Skin%20lesion%20classification%20using%20modified%20deep%20and%20multi-directional%20invariant%20handcrafted%20features&rft.jtitle=Journal%20of%20network%20and%20computer%20applications&rft.au=Pradhan,%20Jitesh&rft.date=2024-11&rft.volume=231&rft.spage=103949&rft.pages=103949-&rft.artnum=103949&rft.issn=1084-8045&rft_id=info:doi/10.1016/j.jnca.2024.103949&rft_dat=%3Celsevier_cross%3ES1084804524001267%3C/elsevier_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c181t-5f8161340fcd70266648ac998ccb26d25b358b473e6306f98582e267bf1ababb3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |