scholarly journals Skin cancer image segmentation utilizing a novel EN-GWO based hyper-parameter optimized FCEDN

2022 ◽  
Author(s):  
Rasmiranjan Mohakud ◽  
Rajashree Dash
Keyword(s):  
Image Segmentation ◽  
Skin Cancer

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 An Empirical Study on Image Segmentation Techniques for Detection of Skin Cancer

2021 ◽  
pp. 71-81
Author(s):  
M. Kavitha ◽  
A. Senthil Arumugam ◽  
V. Saravana Kumar ◽  
S. Anantha Sivaprakasam
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Skin Cancer ◽  
Early Detection ◽  
Empirical Study ◽  
Future Research ◽  
Skin Injury ◽  
Skin Malignancy ◽  
Ideal Approach ◽  
Melanoma Skin

Skin cancer is a crucial predicament in most of western countries including Europe, Australia and America. It is quite often curable whenever perceived and treated early. The significant hazard factors related are skin shading, deficiency of sun-lights, atmosphere, age, and hereditary. The most ideal approach to distinguish melanoma is to perceive another spot in the skin or recognize that is fluctuating in size, shape and shading. Early detection of skin malignancy can stay away from death. Finding of the skin ailment relies upon the extraction of the anomalous skin locale. Right now, methods to separate the skin injury districts are proposed and their outcomes are looked at dependent on the measurable and surface properties. In this study, the myriad kind of features of Dermoscopy image analysis has been thoroughly explores. Moreover, disparity segmentation techniques for detecting Melanoma Skin Cancer are discussed. The ultimate aim of this discussion is to provide suggestions for carrying a future research based about this relevance and limitations.

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 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.

scholarly journals Image segmentation of skin cancer using MobileNet as an encoder and linknet as a decoder

2021 ◽  
Vol 1943 (1) ◽  
pp. 012113
Author(s):  
M Widiansyah ◽  
S Rasyid ◽  
P Wisnu ◽  
A Wibowo
Keyword(s):  
Image Segmentation ◽  
Skin Cancer

scholarly journals Identification of Risk of Occurring Skin Cancer (Melanoma) Using Convolutional Neural Network (CNN)

2021 ◽  
Vol 20 (2) ◽  
pp. 47-51
Author(s):  
Mayinuzzaman Shawon ◽  
Kazi Fakhrul Abedin ◽  
Anik Majumder ◽  
Abir Mahmud ◽  
Md Mahbub Chowdhury Mishu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Skin Cancer ◽  
Convolutional Neural Network ◽  
Cancer Diagnosis ◽  
Detection System ◽  
Median Filter ◽  
Accuracy Score ◽  
Diagnosis System

Skin cancer is one of the most common malignancy in human, has drawn attention from researchers around the world. As skin cancer can turn into fatal if not treated in its earliest stages, the necessity of devising automated skin cancer diagnosis system that can automatically detect skin cancer efficiently in its earliest stage in a faster process than traditional one is of crucial importance. In this paper, a computer aided skin cancer diagnosis system based Convolutional Neural Network method has been shown. Our proposed system consists of five stages namely image acquisition, image preprocessing, image segmentation, feature extraction and classification We remove hair any noise from the images using dull then use median filter to smoothen the images. Next, k-means algorithm was applied for image segmentation on the preprocessed images. Finally, the segmented images were fed into CNN model for feature extraction and classification. The developed system can classify benign and melanoma type skin cancers from Dermoscopic images as accurate as 80.47%. While developing the skin cancer detection system, we compare accuracy score of other models such as Artificial Neural Network (ANN), K-Nearest Neighbor (KNN) and Random Forest with our proposed system. The proposed method has been tested on ‘ISIC Challenge 2016’ test dataset and an accuracy rate of 80.47% was obtained for accurately classifying benign and malignant skin lesions by our proposed model.

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