scholarly journals Melanoma Detection in Dermoscopic Images Using a Cellular Automata Classifier

Computers ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Benjamín Luna-Benoso ◽  
José Cruz Martínez-Perales ◽  
Jorge Cortés-Galicia ◽  
Rolando Flores-Carapia ◽  
Víctor Manuel Silva-García
Keyword(s):  
Feature Extraction ◽  
Skin Cancer ◽  
Cellular Automata ◽  
Sensitivity And Specificity ◽  
Computer Aided Design ◽  
Cancer Type ◽  
Cad System ◽  
Melanoma Detection ◽  
Proposed Model ◽  
Global Patterns

Any cancer type is one of the leading death causes around the world. Skin cancer is a condition where malignant cells are formed in the tissues of the skin, such as melanoma, known as the most aggressive and deadly skin cancer type. The mortality rates of melanoma are associated with its high potential for metastasis in later stages, spreading to other body sites such as the lungs, bones, or the brain. Thus, early detection and diagnosis are closely related to survival rates. Computer Aided Design (CAD) systems carry out a pre-diagnosis of a skin lesion based on clinical criteria or global patterns associated with its structure. A CAD system is essentially composed by three modules: (i) lesion segmentation, (ii) feature extraction, and (iii) classification. In this work, a methodology is proposed for a CAD system development that detects global patterns using texture descriptors based on statistical measurements that allow melanoma detection from dermoscopic images. Image analysis was carried out using spatial domain methods, statistical measurements were used for feature extraction, and a classifier based on cellular automata (ACA) was used for classification. The proposed model was applied to dermoscopic images obtained from the PH2 database, and it was compared with other models using accuracy, sensitivity, and specificity as metrics. With the proposed model, values of 0.978, 0.944, and 0.987 of accuracy, sensitivity and specificity, respectively, were obtained. The results of the evaluated metrics show that the proposed method is more effective than other state-of-the-art methods for melanoma detection in dermoscopic images.

scholarly journals The Study of Recognition Methods Based on Wavelet Transform for Melanoma Detection

2020 ◽  
pp. 46-61
Author(s):  
Maen Almarei ◽  
Khaled Daqrouq
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Skin Cancer ◽  
Probabilistic Neural Network ◽  
Combination Method ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Cancer Type ◽  
Melanoma Detection ◽  
The World

Skin cancer is one of the most cancers occurring in the world. Malignant melanoma is the most skin cancer type causing death around the world. Melanoma could be treated 100% if they are detected at earlier stages. In this paper, various melanoma detection systems were reviewed according to the year of publishing. All reviewed papers were based on feature extraction methods using wavelet transform (WT) in its two versions: Discrete wavelet transform (DWT), and wavelet packet transform (WPT) for melanoma recognition. Our methodology that was based on the WPT feature extraction and probabilistic neural network (PNN) was used for comparison. The ISIC database was used for differentiating between malignant (1110 images) and benign (1110 image) tumors. A (75% training /25% testing) verification system was applied. Many experiments were conducted using different parameters for each experiment. The support vector machine classifier (SVM) was the most common classifier combined with various types of wavelet features that have appeared in many kinds of literature during the last two decades, which achieved relatively the best accuracy ranged between [76% - 98.29%]. In this paper, our combination method of the WPT and entropy was proposed and evaluated. Several experiments were conducted for testing. A comparison manner was used for discussion of the investigation. The proposed method was an excellent detection method for melanoma regarding the complexity, where no preprocessing stage was conducted.

A Comparative Study of Various Techniques used for Melanoma Detection

2017 ◽  
Vol 7 (11) ◽  
pp. 44
Author(s):  
RAINA RAJU K ◽  
S. Swapna Kumar
Keyword(s):  
Feature Extraction ◽  
Skin Cancer ◽  
Normal Skin ◽  
Classification Methods ◽  
Segmentation Method ◽  
Fatal Disease ◽  
Melanoma Detection ◽  
Segmentation Methods ◽  
Different Types ◽  
Extraction Stage

Skin cancer is one of the most fatal disease. It is easily curable, when it is detected in its beginning stage. Early detection of melanoma through accurate techniques and innovative technologies has the greatest potential for decreasing mortality associated with this disease. Mainly there are four steps for detecting melanoma which includes preprocessing, segmentation, feature extraction and classification. The preprocessing stage will remove all the artifacts associated with the lesion. The exact boundaries of lesion are identified from normal skin through segmentation method. Feature extraction stage is used for calculating and obtaining different parameters of the lesion region. The final stage is to classify the lesion as benign or malignant.  In this paper different types of segmentation methods and classification methods are described. Both of these stages are accurately implemented to reach the final detection of the lesion.

A Comparative Study of Various Techniques used for Melanoma Detection

2017 ◽  
Vol 7 (9) ◽  
pp. 52
Author(s):  
Raina Raju K ◽  
S. Swapna Kumar
Keyword(s):  
Feature Extraction ◽  
Skin Cancer ◽  
Normal Skin ◽  
Classification Methods ◽  
Segmentation Method ◽  
Fatal Disease ◽  
Melanoma Detection ◽  
Segmentation Methods ◽  
Different Types ◽  
Extraction Stage

Skin cancer is one of the most fatal disease. It is easily curable, when it is detected in its beginning stage. Early detection of melanoma through accurate techniques and innovative technologies has the greatest potential for decreasing mortality associated with this disease. Mainly there are four steps for detecting melanoma which includes preprocessing, segmentation, feature extraction and classification. The preprocessing stage will remove all the artifacts associated with the lesion. The exact boundaries of lesion are identified from normal skin through segmentation method. Feature extraction stage is used for calculating and obtaining different parameters of the lesion region. The final stage is to classify the lesion as benign or malignant.  In this paper different types of segmentation methods and classification methods are described. Both of these stages are accurately implemented to reach the final detection of the lesion.

A novel melanoma detection model: adapted K-means clustering-based segmentation process

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
S. T. Sukanya ◽  
Jerine
Keyword(s):  
Feature Extraction ◽  
Clustering Algorithm ◽  
Normal Skin ◽  
Detection Model ◽  
Melanoma Detection ◽  
Proposed Model ◽  
Deep Convolution Neural Network ◽  
Local Vector ◽  
Conventional Models ◽  
Occurrence Matrix

AbstractObjectivesThe main intention of this paper is to propose a new Improved K-means clustering algorithm, by optimally tuning the centroids.MethodsThis paper introduces a new melanoma detection model that includes three major phase’s viz. segmentation, feature extraction and detection. For segmentation, this paper introduces a new Improved K-means clustering algorithm, where the initial centroids are optimally tuned by a new algorithm termed Lion Algorithm with New Mating Process (LANM), which is an improved version of standard LA. Moreover, the optimal selection is based on the consideration of multi-objective including intensity diverse centroid, spatial map, and frequency of occurrence, respectively. The subsequent phase is feature extraction, where the proposed Local Vector Pattern (LVP) and Grey-Level Co-Occurrence Matrix (GLCM)-based features are extracted. Further, these extracted features are fed as input to Deep Convolution Neural Network (DCNN) for melanoma detection.ResultsFinally, the performance of the proposed model is evaluated over other conventional models by determining both the positive as well as negative measures. From the analysis, it is observed that for the normal skin image, the accuracy of the presented work is 0.86379, which is 47.83% and 0.245% better than the traditional works like Conventional K-means and PA-MSA, respectively.ConclusionsFrom the overall analysis it can be observed that the proposed model is more robust in melanoma prediction, when compared over the state-of-art models.

Artificial Intelligence for Skin Cancer Detection: A Scoping Review (Preprint)

2020 ◽  
Author(s):  
Abdulrahman Takiddin ◽  
Jens Schneider ◽  
Yin Yang ◽  
Alaa Abd-Alrazaq ◽  
Mowafa Househ
Keyword(s):  
Artificial Intelligence ◽  
Skin Cancer ◽  
Scoping Review ◽  
Performance Metrics ◽  
Data Extraction ◽  
Characteristic Curve ◽  
Evaluation Metrics ◽  
Cancer Type ◽  
Computer Algorithms ◽  
Scoping Reviews

BACKGROUND Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, Artificial Intelligence (AI) tools are being used, including shallow and deep machine learning-based techniques that are trained to detect and classify skin cancer using computer algorithms and deep neural networks. OBJECTIVE The aim of this study is to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examines the reliability of the selected papers by studying the correlation between the dataset size and number of diagnostic classes with the performance metrics used to evaluate the models. METHODS We conducted a systematic search for articles using IEEE Xplore, ACM DL, and Ovid MEDLINE databases following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. The study included in this scoping review had to fulfill several selection criteria; to be specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were conducted by two reviewers independently. Extracted data were synthesized narratively, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics. RESULTS We retrieved 906 papers from the 3 databases, but 53 studies were eligible for this review. While shallow techniques were used in 14 studies, deep techniques were utilized in 39 studies. The studies used accuracy (n=43/53), the area under receiver operating characteristic curve (n=5/53), sensitivity (n=3/53), and F1-score (n=2/53) to assess the proposed models. Studies that use smaller datasets and fewer diagnostic classes tend to have higher reported accuracy scores. CONCLUSIONS The adaptation of AI in the medical field facilitates the diagnosis process of skin cancer. However, the reliability of most AI tools is questionable since small datasets or low numbers of diagnostic classes are used. In addition, a direct comparison between methods is hindered by a varied use of different evaluation metrics and image types.

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

scholarly journals Precise Identification of Prostate Cancer from DWI Using Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3664
Author(s):  
Islam R. Abdelmaksoud ◽  
Ahmed Shalaby ◽  
Ali Mahmoud ◽  
Mohammed Elmogy ◽  
Ahmed Aboelfetouh ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Transfer Learning ◽  
Sensitivity And Specificity ◽  
Benign Tumors ◽  
B Values ◽  
Cad System ◽  
Average Accuracy ◽  
Apparent Diffusion ◽  
Invasive Techniques ◽  
Fine Tune

Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions. Then, discriminatory features in the form of apparent diffusion coefficient (ADC) volumes are estimated from the segmented regions. The ADC maps that constitute these volumes are labeled by a radiologist to identify the ADC maps with malignant or benign tumors. Finally, transfer learning is used to fine-tune two different previously-trained convolutional neural network (CNN) models (AlexNet and VGGNet) for detecting and identifying prostate cancer. Results: Multiple experiments were conducted to evaluate the accuracy of different CNN models using DWI datasets acquired at nine distinct b-values that included both high and low b-values. The average accuracy of AlexNet at the nine b-values was 89.2±1.5% with average sensitivity and specificity of 87.5±2.3% and 90.9±1.9%. These results improved with the use of the deeper CNN model (VGGNet). The average accuracy of VGGNet was 91.2±1.3% with sensitivity and specificity of 91.7±1.7% and 90.1±2.8%. Conclusions: The results of the conducted experiments emphasize the feasibility and accuracy of the developed system and the improvement of this accuracy using the deeper CNN.

Chinese Image Captioning via Fuzzy Attention-based DenseNet-BiLSTM

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-18 ◽  
Author(s):  
Huimin Lu ◽  
Rui Yang ◽  
Zhenrong Deng ◽  
Yonglin Zhang ◽  
Guangwei Gao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Contextual Information ◽  
Image Features ◽  
Context Information ◽  
Global Information ◽  
Image Description ◽  
Image Captioning ◽  
Image Content ◽  
Single Feature ◽  
Proposed Model

Chinese image description generation tasks usually have some challenges, such as single-feature extraction, lack of global information, and lack of detailed description of the image content. To address these limitations, we propose a fuzzy attention-based DenseNet-BiLSTM Chinese image captioning method in this article. In the proposed method, we first improve the densely connected network to extract features of the image at different scales and to enhance the model’s ability to capture the weak features. At the same time, a bidirectional LSTM is used as the decoder to enhance the use of context information. The introduction of an improved fuzzy attention mechanism effectively improves the problem of correspondence between image features and contextual information. We conduct experiments on the AI Challenger dataset to evaluate the performance of the model. The results show that compared with other models, our proposed model achieves higher scores in objective quantitative evaluation indicators, including BLEU , BLEU , METEOR, ROUGEl, and CIDEr. The generated description sentence can accurately express the image content.

Product Model Preparation and Processing for Micromanufacturing

2008 ◽  
Vol 8 (2) ◽  
Author(s):  
Chao-Yaug Liao ◽  
Jean-Claude Léon ◽  
Cédric Masclet ◽  
Michel Bouriau ◽  
Patrice L. Baldeck ◽  
...  
Keyword(s):  
Laser Beam ◽  
Computer Aided Design ◽  
Welding Process ◽  
Product Model ◽  
Processing Scheme ◽  
Proposed Model ◽  
Layer Manufacturing ◽  
Manifold Models ◽  
Model Preparation ◽  
Aided Design

Based on the two-photon polymerization technique, an analysis of product shapes is performed so that their digital manufacturing models can be efficiently processed for micromanufacture. To describe microstructures, this analysis shows that nonmanifold models are of interest. These models can be intuitively understood as combinations of wires, surfaces, and volumes. Minimum acceptable wall thickness, wire dimension, and laser density of energy are among the elements justifying this category of models. Taking into account this requirement, a model preparation and processing scheme is proposed that widens the laser beam trajectories with a concept of extended layer manufacturing technique. A tessellation process suited for non-manifold models has been developed for computer-aided design models imported from standard for the exchange of product files. After tessellation, several polyhedral subdomains form a nonmanifold polyhedron. To plan the trajectories of the laser beam, adaptive slicing and global 3D hatching processes as well as a “welding” process (for joining subdomains of different dimensionality) have been combined. Finally, two nonmanifold microstructures are fabricated according to the proposed model preparation and processing scheme.

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