scholarly journals Skin Cancer Image Segmentation Based on Symmetrical Threshold Contour Algorithm

2020 ◽  
Vol 9 (1) ◽  
pp. 2390-2398
Keyword(s):  
Image Segmentation ◽  
Skin Cancer ◽  
Skin Lesion ◽  
Contour Detection ◽  
Cancerous Lesion ◽  
Image Contour ◽  
Thresholding Algorithm ◽  
Cancer Lesion ◽  
Lesion Skin

Image segmentation is a process of identifying sub patterns in a given image. The purpose of skin cancer image segmentation is to represent it ina meaningful way for effectiveanalysis. Segmentation of skin cancer image is mostly used to detect the boundaries and objects present in a skin lesion. This approach describes the skin cancer image segmentation based on symmetrical threshold contour algorithm with similar thresholding values for segmentation of the accurate cancerous lesion. Skin cancer lesion shape and structure is the most important parameter in this method. In this paper, skin cancer image contour detection isbased on symmetrical thresholding algorithm using MATLAB soft ware.

scholarly journals Deep Hybrid Convolutional Neural Network for Segmentation of Melanoma Skin Lesion

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Cheng-Hong Yang ◽  
Jai-Hong Ren ◽  
Hsiu-Chen Huang ◽  
Li-Yeh Chuang ◽  
Po-Yin Chang
Keyword(s):  
Image Segmentation ◽  
Skin Cancer ◽  
Skin Lesion ◽  
Survival Rates ◽  
Skin Lesions ◽  
Manual Segmentation ◽  
Cure Rate ◽  
Dice Coefficient ◽  
Skin Imaging ◽  
Low Efficiency

Melanoma is a type of skin cancer that often leads to poor prognostic responses and survival rates. Melanoma usually develops in the limbs, including in fingers, palms, and the margins of the nails. When melanoma is detected early, surgical treatment may achieve a higher cure rate. The early diagnosis of melanoma depends on the manual segmentation of suspected lesions. However, manual segmentation can lead to problems, including misclassification and low efficiency. Therefore, it is essential to devise a method for automatic image segmentation that overcomes the aforementioned issues. In this study, an improved algorithm is proposed, termed EfficientUNet++, which is developed from the U-Net model. In EfficientUNet++, the pretrained EfficientNet model is added to the UNet++ model to accelerate segmentation process, leading to more reliable and precise results in skin cancer image segmentation. Two skin lesion datasets were used to compare the performance of the proposed EfficientUNet++ algorithm with other common models. In the PH2 dataset, EfficientUNet++ achieved a better Dice coefficient (93% vs. 76%–91%), Intersection over Union (IoU, 96% vs. 74%–95%), and loss value (30% vs. 44%–32%) compared with other models. In the International Skin Imaging Collaboration dataset, EfficientUNet++ obtained a similar Dice coefficient (96% vs. 94%–96%) but a better IoU (94% vs. 89%–93%) and loss value (11% vs. 13%–11%) than other models. In conclusion, the EfficientUNet++ model efficiently detects skin lesions by improving composite coefficients and structurally expanding the size of the convolution network. Moreover, the use of residual units deepens the network to further improve performance.

scholarly journals Detection of Melanoma Skin Cancer using Convolutional Neural Network algorithm

2020 ◽  
Vol 9 (7) ◽  
pp. 115-118
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Skin Cancer ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Health Sector ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Melanoma Skin

Skin Cancer, a health issue which might cause severe consequences if not detected and controlled properly. Since there is a huge evolution in the health sector because of development in computer technologies, it is possible to analyze images efficiently and make correct decisions. Deep learning algorithms can be used for analyzing dermoscopic images by learning features of images in an incremental manner. Aim of our proposed method is to categorize skin lesion image as Benign or Melanoma and also to study the performance of Convolutional Neural Network algorithm using data augmentation technique and without data augmentation technique. The proposed method uses dataset from ISIC archive 2019. Steps involved in the proposed method are Image Pre-Processing, Image Segmentation and Image Classification. Initially, Image Pre-Processing algorithm is performed on skin lesion image. Image Segmentation algorithm is used to obtain Region of Interest (ROI) from pre-processed image. Then, Convolutional Neural Network algorithm classifies image as melanoma or benign. The Proposed method can rapidly detect melanoma skin cancer which aids in starting the treatment process without delay.

Border Detection in Skin Lesion Images Using an Improved Clustering Algorithm

2020 ◽  
Vol 16 (4) ◽  
pp. 15-29
Author(s):  
Jayalakshmi D. ◽  
Dheeba J.
Keyword(s):  
Skin Cancer ◽  
Skin Lesion ◽  
Clustering Algorithm ◽  
Median Filter ◽  
Second Phase ◽  
Border Detection ◽  
Early Computer ◽  
Segmentation Methods ◽  
Two Phases ◽  
Skin Cancer Detection

The incidence of skin cancer has been increasing in recent years and it can become dangerous if not detected early. Computer-aided diagnosis systems can help the dermatologists in assisting with skin cancer detection by examining the features more critically. In this article, a detailed review of pre-processing and segmentation methods is done on skin lesion images by investigating existing and prevalent segmentation methods for the diagnosis of skin cancer. The pre-processing stage is divided into two phases, in the first phase, a median filter is used to remove the artifact; and in the second phase, an improved K-means clustering with outlier removal (KMOR) algorithm is suggested. The proposed method was tested in a publicly available Danderm database. The improved cluster-based algorithm gives an accuracy of 92.8% with a sensitivity of 93% and specificity of 90% with an AUC value of 0.90435. From the experimental results, it is evident that the clustering algorithm has performed well in detecting the border of the lesion and is suitable for pre-processing dermoscopic images.

scholarly journals Pattern and Frequency of Skin Malignancies at Laboratory of Pathology Anatomy Dr. Kariadi General Hospital in 2008-2009

Sains Medika ◽  
2015 ◽  
Vol 6 (1) ◽  
pp. 21
Author(s):  
Susilorini Susilorini ◽  
Udadi Sadhana ◽  
Indra Widjaya
Keyword(s):  
Squamous Cell Carcinoma ◽  
Skin Cancer ◽  
Skin Lesion ◽  
Cell Carcinoma ◽  
General Hospital ◽  
Squamous Cell ◽  
Medical Records ◽  
Secondary Data ◽  
Cross Sectional ◽  
Neoplastic Lesion

Introduction: A periodical database is important including for skin cancer. Periodical registration is needed to acknowledge changes in pattern and frequencies of skin lesion. Objective: The purpose of this study was to describe the pattern and the frequency of skin lesion in RSUD Kariadi.Method: A cross-sectional study was conducted through analysis of the medical records of patients diagnosed skin lesion in the pathology labolatory of RSUD Kariadi between 2008 and 2009. The variables were secondary data including age, gender, specimen area, dan histopathology diagnosis. Data was choosen by consecutive sampling from 381 medical records of skin tissues examined at laboratorium of pathology anatomy of Dr. Kariadi general hospital during 2008-2009.Result: 381 cases were recorded comprising of 246 (65%) neoplastic and 135 (35%) non neoplastic lesion. 120 patients presented with skin cancer, and 126 with benign skin lesion. Most malignancy was observed among female patients (62.5%) on age catagory of 15-39 (65%). The most common lesion was basal cell carcinoma (48.3%) followed by squamous cell carcinoma (33.3%), malignant melanoma (10%), skin appendix carcinoma (2.5%), other malignancies (4.9%).Conclusion: the most common malignancies in Dr. Kariadi general hospital before 2008 was similar to data from 13 laboratory of pathology anatomy in Indonesia, which is squamous cell carcinoma.

Proposed Threshold Algorithm for Accurate Segmentation for Skin Lesion

2015 ◽  
Vol 4 (2) ◽  
pp. 40-47
Author(s):  
T. Y. Satheesha ◽  
D. Sathyanarayana ◽  
M. N. Giri Prasad
Keyword(s):  
Skin Cancer ◽  
Skin Lesion ◽  
Skin Lesions ◽  
Large Set ◽  
Threshold Values ◽  
Diagnostic Systems ◽  
Automated Diagnosis ◽  
Algorithm Efficiency ◽  
Threshold Algorithms ◽  
Threshold Technique

Automated diagnosis of skin cancer can be easily achieved only by effective segmentation of skin lesion. But this is a highly challenging task due to the presence of intensity variations in the images of skin lesions. The authors here, have presented a histogram analysis based fuzzy C mean threshold technique to overcome the drawbacks. This not only reduces the computational complexity but also unifies advantages of soft and hard threshold algorithms. Calculation of threshold values even the presence of abrupt intensity variations is simplified. Segmentation of skin lesions is easily achieved, in a more efficient way in the following algorithm. The experimental verification here is done on a large set of skin lesion images containing every possible artifacts which highly contributes to reversed segmentation outputs. This algorithm efficiency was measured based on a comparison with other prominent threshold methods. This approach has performed reasonably well and can be implemented in the expert skin cancer diagnostic systems

Simplification of neural networks for skin lesion image segmentation using color channel pruning

2020 ◽  
Vol 82 ◽  
pp. 101729
Author(s):  
M. Hajabdollahi ◽  
R. Esfandiarpoor ◽  
P. Khadivi ◽  
S.M.R. Soroushmehr ◽  
N. Karimi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Skin Lesion ◽  
Color Channel

scholarly journals Dermoscopic skin lesion image segmentation based on Local Binary Pattern Clustering: Comparative study

2020 ◽  
Vol 59 ◽  
pp. 101924 ◽  
Author(s):  
Pedro M.M. Pereira ◽  
Rui Fonseca-Pinto ◽  
Rui Pedro Paiva ◽  
Pedro A.A. Assuncao ◽  
Luis M.N. Tavora ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Skin Lesion ◽  
Comparative Study ◽  
Local Binary Pattern ◽  
Pattern Clustering
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