Skin Melanoma Classification System Using Deep Learning

Manjula, D.

doi:10.32604/cmc.2021.015503

Cited by 13 publications

(13 citation statements)

References 20 publications

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“…CNN has been among the most popular classification methods in recent years. The CNN method was preferred in this study because it can successfully solve many classification problems [13][14][15][16][17][18][19]. Based on the results of this study, it is a suitable method for solving the galaxy classification problem.…”

Section: Structurementioning

confidence: 99%

A Hyperparameter Optimization for Galaxy Classification

Şenel¹

2023

Computers, Materials &Amp; Continua

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In this study, the morphological galaxy classification process was carried out with a hybrid approach. Since the Galaxy classification process may contain detailed information about the universe's formation, it remains the current research topic. Researchers divided more than 100 billion galaxies into ten different classes. It is not always possible to understand which class the galaxy types belong. However, Artificial Intelligence (AI) can be used for successful classification. There are studies on the automatic classification of galaxies into a small number of classes. As the number of classes increases, the success of the used methods decreases. Based on the literature, the classification using Convolutional Neural Network (CNN) is better. Three metaheuristic algorithms are used to obtain the optimum architecture of CNN. These are Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms. A CNN architecture with nine hidden layers and two full connected layers was used. The number of neurons in the hidden layers and the fully connected layers, the learning coefficient and the batch size values were optimized. The classification accuracy of my model was 85%. The best results were obtained using GWO. Manual optimization of CNN is difficult. It was carried out with the help of the GWO meta-heuristic algorithm.

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Section: Structurementioning

confidence: 99%

A Hyperparameter Optimization for Galaxy Classification

Şenel¹

2023

Computers, Materials &Amp; Continua

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“…A simple median filtering [16,17] is employed in this stage. In many pattern recognition approaches, it is often used to remove noises due to its simplicity and easy computation.…”

Section: Preprocessingmentioning

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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“…After extracting the energy features for a particular level and directions from the two groups (normal and abnormal or low-risk and high-risk) of MRI images, t-test is applied to identify the best sub-band that has the significant different between the two groups. The t-test [24] is defined in Eq. ( 5).…”

Section: Sub-band Selectionmentioning

confidence: 99%

Bendlets and Ensemble Learning Based MRI Brain Classification System

Muthaiyan¹,

Malleswaran²

2022

Intelligent Automation &Amp; Soft Computing

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Brain tumours are composed of cells where the growth is unrestrained. Though the incidence rate is lower, it is a serious threatening disease to human lives. For effective treatment, an accurate and quick method to classify Magnetic Resonance Imaging (MRI) is required. To identify the meaningful patterns and to interpret images, pattern recognition algorithms are developed. In this work, an extension of Shearlet transform named Bendlets is employed to interpret MRI images and decision making is done by ensemble learning using k-Nearest Neighbor (kNN), Naive Bayesian and Support Vector Machine (SVM) classifiers. The Bendlet and Ensemble Learning (BEL) based system utilizes Bendlet Co-Occurrence Features (BCFs) and Histograms of Positive and Negative Bendlet Coefficients (HPBC & HNBC) from the dominant sub-band as texture descriptors. The rate of classification by the BEL system for the 200 images from REpository of Molecular BRAin Neoplasia DaTa (REMBRANDT) is 99.5% at the initial stage (normal/abnormal classification) and 99% at the final stage (low-risk/high-risk classification). Based on the results, the implementation of BEL system could provide continuous monitoring of the progress of brain tumour very effectively and also offers a real-time response.

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Skin Melanoma Classification System Using Deep Learning

Cited by 13 publications

References 20 publications

A Hyperparameter Optimization for Galaxy Classification

A Hyperparameter Optimization for Galaxy Classification

Faster Region Based Convolutional Neural Network for Skin Lesion Segmentation

Bendlets and Ensemble Learning Based MRI Brain Classification System

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