PMID- 37835884
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231103
IS  - 2075-4418 (Print)
IS  - 2075-4418 (Electronic)
IS  - 2075-4418 (Linking)
VI  - 13
IP  - 19
DP  - 2023 Oct 6
TI  - A Comparative Analysis of Skin Cancer Detection Applications Using 
      Histogram-Based Local Descriptors.
LID - 10.3390/diagnostics13193142 [doi]
LID - 3142
AB  - Among the most serious types of cancer is skin cancer. Despite the risk of death, 
      when caught early, the rate of survival is greater than 95%. This inspires 
      researchers to explore methods that allow for the early detection of skin cancer 
      that could save millions of lives. The ability to detect the early signs of skin 
      cancer has become more urgent in light of the rising number of illnesses, the 
      high death rate, and costly healthcare treatments. Given the gravity of these 
      issues, experts have created a number of existing approaches for detecting skin 
      cancer. Identifying skin cancer and whether it is benign or malignant involves 
      detecting features of the lesions such as size, form, symmetry, color, etc. The 
      aim of this study is to determine the most successful skin cancer detection 
      methods by comparing the outcomes and effectiveness of the various applications 
      that categorize benign and malignant forms of skin cancer. Descriptors such as 
      the Local Binary Pattern (LBP), the Local Directional Number Pattern (LDN), the 
      Pyramid of Histogram of Oriented Gradients (PHOG), the Local Directional Pattern 
      (LDiP), and Monogenic Binary Coding (MBC) are used to extract the necessary 
      features. Support vector machines (SVM) and XGBoost are used in the 
      classification process. In addition, this study uses colored histogram-based 
      features to classify the various characteristics obtained from the color images. 
      In the experimental results, the applications implemented with the proposed color 
      histogram-based features were observed to be more successful. Under the proposed 
      method (the colored LDN feature obtained using the YCbCr color space with the 
      XGBoost classifier), a 90% accuracy rate was achieved on Dataset 1, which was 
      obtained from the Kaggle website. For the HAM10000 data set, an accuracy rate of 
      96.50% was achieved under a similar proposed method (the colored MBC feature 
      obtained using the HSV color space with the XGBoost classifier).
FAU - Aydin, Yildiz
AU  - Aydin Y
AD  - Department of Computer Engineering, Erzincan Binali Yildirim University, Erzincan 
      24000, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20231006
PL  - Switzerland
TA  - Diagnostics (Basel)
JT  - Diagnostics (Basel, Switzerland)
JID - 101658402
PMC - PMC10572674
OTO - NOTNLM
OT  - color features
OT  - histogram-based local descriptors
OT  - identification
OT  - skin cancer
COIS- The authors declare no conflict of interest.
EDAT- 2023/10/14 10:47
MHDA- 2023/10/14 10:48
PMCR- 2023/10/06
CRDT- 2023/10/14 01:14
PHST- 2023/08/11 00:00 [received]
PHST- 2023/10/01 00:00 [revised]
PHST- 2023/10/04 00:00 [accepted]
PHST- 2023/10/14 10:48 [medline]
PHST- 2023/10/14 10:47 [pubmed]
PHST- 2023/10/14 01:14 [entrez]
PHST- 2023/10/06 00:00 [pmc-release]
AID - diagnostics13193142 [pii]
AID - diagnostics-13-03142 [pii]
AID - 10.3390/diagnostics13193142 [doi]
PST - epublish
SO  - Diagnostics (Basel). 2023 Oct 6;13(19):3142. doi: 10.3390/diagnostics13193142.