Skin Lesion Segmentation by Pixel by Pixel Approach Using Deep Learning

Justin, Shekaina; Pattnaik, Manjula

doi:10.29284/ijasis.6.1.2020.12-20

Cited by 17 publications

(6 citation statements)

References 12 publications

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“…6 shows a sample image in three categories in the PH2 database. A random split approach is employed with 70:30 ratios to obtain The commonly used metrics of segmentation based approaches are Pixel Accuracy (PA) and Jaccard Index (JI) [21]. Their definitions are as follows: PA: It is the ratio between the number of pixels correctly classified as lesion and the total number of pixels marked as lesion in the respective ground truth data.…”

Section: Resultsmentioning

confidence: 99%

Faster Region Based Convolutional Neural Network for Skin Lesion Segmentation

Murugesan¹,

Jeyapriya²,

Hemalatha³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The diagnostic interpretation of dermoscopic images is a complex task as it is very difficult to identify the skin lesions from the normal. Thus the accurate detection of potential abnormalities is required for patient monitoring and effective treatment. In this work, a Two-Tier Segmentation (TTS) system is designed, which combines the unsupervised and supervised techniques for skin lesion segmentation. It comprises preprocessing by the median filter, TTS by Colour K-Means Clustering (CKMC) for initial segmentation and Faster Region based Convolutional Neural Network (FR-CNN) for refined segmentation. The CKMC approach is evaluated using the different number of clusters (k = 3, 5, 7, and 9). An inception network with batch normalization is employed to segment melanoma regions effectively. Different loss functions such as Mean Absolute Error (MAE), Cross Entropy Loss (CEL), and Dice Loss (DL) are utilized for performance evaluation of the TTS system. The anchor box technique is employed to detect the melanoma region effectively. The TTS system is evaluated using 200 dermoscopic images from the PH 2 database. The segmentation accuracies are analyzed in terms of Pixel Accuracy (PA) and Jaccard Index (JI). Results show that the TTS system has 90.19% PA with 0.8048 JI for skin lesion segmentation using DL in FR-CNN with seven clusters in CKMC than CEL and MAE.

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Section: Resultsmentioning

confidence: 99%

Faster Region Based Convolutional Neural Network for Skin Lesion Segmentation

Murugesan¹,

Jeyapriya²,

Hemalatha³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…Deep learning architecture is employed in many medical image analysis systems such as pneumonia classification [15], mammogram classification [16], skin cancer [17,18], Covid-19 diagnosis [19], and vascular tissue simulation model [20]. They use a neural network for the classification and convolution and max pooling layer to extract deep features.…”

Section: Image/signal Acquisitionmentioning

confidence: 99%

Horizontal Voting Ensemble Based Predictive Modeling System for Colon燙ancer

Eswaran¹,

Anand²

2023

Computer Systems Science and Engineering

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Colon cancer is the third most commonly diagnosed cancer in the world. Most colon AdenoCArcinoma (ACA) arises from pre-existing benign polyps in the mucosa of the bowel. Thus, detecting benign at the earliest helps reduce the mortality rate. In this work, a Predictive Modeling System (PMS) is developed for the classification of colon cancer using the Horizontal Voting Ensemble (HVE) method. Identifying different patterns in microscopic images is essential to an effective classification system. A twelve-layer deep learning architecture has been developed to extract these patterns. The developed HVE algorithm can increase the system's performance according to the combined models from the last epochs of the proposed architecture. Ten thousand (10000) microscopic images are taken to test the classification performance of the proposed PMS with the HVE method. The microscopic images obtained from the colon tissues are classified into ACA or benign by the proposed PMS. Results prove that the proposed PMS has ∼8% performance improvement over the architecture without using the HVE method. The proposed PMS for colon cancer reduces the misclassification rate and attains 99.2% of sensitivity and 99.4% of specificity. The overall accuracy of the proposed PMS is 99.3%, and without using the HVE method, it is only 91.3%.

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“…Neural network based CADs are probably the most successful supervised based medical diagnosis system [13][14][15][16][17][18][19] in terms of their accuracy. In general, a neural network based CAD works as follows:…”

Section: Classificationmentioning

confidence: 99%

“…The available CAD systems can be categorized into supervised or unsupervised systems based on the classifier used in the detection stage. Currently, neural network based supervised CAD systems [13][14][15][16][17][18][19] are most successful. However, they are not very efficient at learning and adopting continuous changes.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Artificial Intelligence Model for Skin Cancer Diagnosis

Lakshmi¹,

Jasmine²

2021

Computer Systems Science and Engineering

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Melanoma or skin cancer is the most dangerous and deadliest disease. As the incidence and mortality rate of skin cancer increases worldwide, an automated skin cancer detection/classification system is required for early detection and prevention of skin cancer. In this study, a Hybrid Artificial Intelligence Model (HAIM) is designed for skin cancer classification. It uses diverse multi-directional representation systems for feature extraction and an efficient Exponentially Weighted and Heaped Multi-Layer Perceptron (EWHMLP) for the classification. Though the wavelet transform is a powerful tool for signal and image processing, it is unable to detect the intermediate dimensional structures of a medical image. Thus the proposed HAIM uses Curvelet (CurT), Contourlet (ConT) and Shearlet (SheT) transforms as feature extraction techniques. Though MLP is very flexible and well suitable for the classification problem, the learning of weights is a challenging task. Also, the optimization process does not converge, and the model may not be stable. To overcome these drawbacks, EWHMLP is developed. Results show that the combined qualities of each transform in a hybrid approach provides an accuracy of 98.33% in a multi-class approach on PH 2 database.

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Skin Lesion Segmentation by Pixel by Pixel Approach Using Deep Learning

Cited by 17 publications

References 12 publications

Faster Region Based Convolutional Neural Network for Skin Lesion Segmentation

Faster Region Based Convolutional Neural Network for Skin Lesion Segmentation

Horizontal Voting Ensemble Based Predictive Modeling System for Colon燙ancer

A Hybrid Artificial Intelligence Model for Skin Cancer Diagnosis

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