scholarly journals Computer Aided System for Malignant Melanoma Detection

2021 ◽  
pp. 72-75
Author(s):  
Mrs. Latha S S ◽  
Harshitha S ◽  
N Preetha ◽  
R Yashaswini ◽  
Nitika Bharati
Keyword(s):  
Skin Cancer ◽  
Early Stage ◽  
Wavelet Packet ◽  
Principal Component ◽  
Support Vector ◽  
Melanoma Detection ◽  
Skin Cancers ◽  
Boundary Tracing ◽  
Processing Techniques ◽  
Cure Rates

Melanoma is one of the skin cancers that attacks the cells of melanocytes that produce skin-forming pigments. In this project a new intelligent method of classifying melanoma lesions is implemented. The system consists of four stages; image pre-processing, image segmentation, feature extraction, and image classification. As the first step of the image analysis, pre-processing techniques are implemented to remove noise and undesired structures from the images using techniques such as median filtering and contrast enhancement. In the second step, a simple thresholding method is used to segment and localize the lesion, a boundary tracing algorithm is also implemented to validate the segmentation. Then, a wavelet approach is used to extract the features, more specifically Wavelet Packet Transform (WPT).Finally, the dimensionality of the selected features is reduced with Principal Component Analysis(PCA) and later supplied to an Artificial Neural Network and Support Vector Machine classifiers for classification. Incident rates of melanoma skin cancer have been rising since last two decades. So, early, fast and effective detection of skin cancer is paramount importance. If detected at an early stage. Skin has one of the highest cure rates, and the most cases, the treatment is quite simple and involves excision of the lesion. Moreover, at an early stage, skin cancer is very economical to treat, while at a late stage, cancerous lesions usually result in near fatal consequences and extremely high cost associated with the necessary treatments.

scholarly journals PSO-SVM hybrid system for melanoma detection from histo-pathological images

2019 ◽  
Vol 9 (4) ◽  
pp. 2941
Author(s):  
Maen Takruri ◽  
Mohamed Khaled Abu Mahmoud ◽  
Adel Al-Jumaily
Keyword(s):  
Hybrid System ◽  
Wavelet Packet ◽  
Automated System ◽  
Support Vector ◽  
Svm Classifier ◽  
Pathology Laboratory ◽  
Swarm Optimization ◽  
Image Histogram ◽  
Melanoma Detection ◽  
Occurrence Matrix

This paper introduces an automated system for skin cancer (melanoma) detection from Histo-pathological images sampled from microscopic slides of skin biopsy. The proposed system is a hybrid system based on Particle Swarm Optimization and Support Vector Machine (PSO-SVM). The features used are extracted from the grayscale image histogram, the co-occurrence matrix and the energy of the wavelet coefficients resulting from the wavelet packet decomposition. The PSO-SVM system selects the best feature set and the best values for the SVM parameters (C and γ) that optimize the performance of the SVM classifier.   The system performance is tested on a real dataset obtained from the Southern Pathology Laboratory in Wollongong NSW, Australia. Evaluation results show a classification accuracy of 87.13%, a sensitivity of 94.1% and a specificity of 80.22%.The sensitivity and specificity results are comparable to those obtained by dermatologists.

scholarly journals Skin Cancer Recognition Using SVM Image Processing Technique

2020 ◽  
pp. 11-15
Author(s):  
Rahul Chand Thakur ◽  
◽  
Vaibhav Panwar ◽  
Keyword(s):  
Image Processing ◽  
Skin Cancer ◽  
Supervised Learning ◽  
Detection System ◽  
Early Stage ◽  
Image Features ◽  
The Body ◽  
Image Processing Technique ◽  
Support Vector ◽  
Skin Cells

Skin cancer is considered as commonest cause of death among humans in today's world. This type of cancer shows non uniform or patchy growth of skin cells that most commonly occurs on of the certain parts of body which are more likely exposed to the light, but it can occur anywhere on the body. The majority of skin cancers can be treated if detected early. As a result, finding skin cancer early and easily will save a patient's life. Early detection of skin cancer at an early stage is now possible thanks to modern technologies. Biopsy procedure [1] is a systematic method for diagnosis skin cancer. It is achieved by extracting skin cells, after which the sample is sent to different laboratories for examination. It's a very long (in terms of time) and painful process. For primitive detection of skin cancer disease, we proposed a skin cancer detection system based on svm. It is more helpful to patients. Various methods of image processing and the supervised learning algorithm called Support Vector Machine (SVM) are used in the identification process. Epiluminescence microscopy is taken using an image and particular to several preprocessing techniques which are used in the reduction of sound artifacts and improvise quality of images. Segmentation is done by using certain thresholding techniques like OTSU. The GLCM technique must be used to remove certain image features. These characteristics are fed into the classifier as input. The Supervised learning model called (SVM) is used to distinguish data sets. It determines whether a picture is cancerous or not.

scholarly journals A multi-tree genetic programming representation for melanoma detection using local and global features

2020 ◽  
Author(s):  
Q Ul Ain ◽  
Harith Al-Sahaf ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Skin Cancer ◽  
Genetic Programming ◽  
Early Stage ◽  
Extraction Methods ◽  
Image Features ◽  
Color Variation ◽  
Genetic Operators ◽  
Global Features ◽  
Melanoma Detection ◽  
Performance Gains

© Springer Nature Switzerland AG 2018. Melanoma is the deadliest type of skin cancer that accounts for nearly 75% of deaths associated with it. However, survival rate is high, if diagnosed at an early stage. This study develops a novel classification approach to melanoma detection using a multi-tree genetic programming (GP) method. Existing approaches have employed various feature extraction methods to extract features from skin cancer images, where these different types of features are used individually for skin cancer image classification. However they remain unable to use all these features together in a meaningful way to achieve performance gains. In this work, Local Binary Pattern is used to extract local information from gray and color images. Moreover, to capture the global information, color variation among the lesion and skin regions, and geometrical border shape features are extracted. Genetic operators such as crossover and mutation are designed accordingly to fit the objectives of our proposed method. The performance of the proposed method is assessed using two skin image datasets and compared with six commonly used classification algorithms as well as the single tree GP method. The results show that the proposed method significantly outperformed all these classification methods. Being interpretable, this method may help dermatologist identify prominent skin image features, specific to a type of skin cancer.

scholarly journals Non-Melanoma Skin Cancer in People Living With HIV: From Epidemiology to Clinical Management

2021 ◽  
Vol 11 ◽  
Author(s):  
Emmanuele Venanzi Rullo ◽  
Maria Grazia Maimone ◽  
Francesco Fiorica ◽  
Manuela Ceccarelli ◽  
Claudio Guarneri ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Early Stage ◽  
Treatment Options ◽  
Case Series ◽  
Surgical Excision ◽  
People Living With Hiv ◽  
Skin Cancers ◽  
Ablative Techniques ◽  
Living With Hiv ◽  
Melanoma Skin

Skin cancers represent the most common human tumors with a worldwide increasing incidence. They can be divided into melanoma and non-melanoma skin cancers (NMSCs). NMSCs include mainly squamous cell (SCC) and basal cell carcinoma (BCC) with the latest representing the 80% of the diagnosed NMSCs. The pathogenesis of NMSCs is clearly multifactorial. A growing body of literature underlies a crucial correlation between skin cancer, chronic inflammation and immunodeficiency. Intensity and duration of immunodeficiency plays an important role. In immunocompromised patients the incidence of more malignant forms or the development of multiple tumors seems to be higher than among immunocompetent patients. With regards to people living with HIV (PLWH), since the advent of combined antiretroviral therapy (cART), the incidence of non-AIDS-defining cancers (NADCs), such as NMSCs, have been increasing and now these neoplasms represent a leading cause of illness in this particular population. PLWH with NMSCs tend to be younger, to have a higher risk of local recurrence and to have an overall poorer outcome. NMSCs show an indolent clinical course if diagnosed and treated in an early stage. BCC rarely metastasizes, while SCC presents a 4% annual incidence of metastasis. Nevertheless, metastatic forms lead to poor patient outcome. NMSCs are often treated with full thickness treatments (surgical excision, Mohs micro-graphic surgery and radiotherapy) or superficial ablative techniques (such as cryotherapy, electrodesiccation and curettage). Advances in genetic landscape understanding of NMSCs have favored the establishment of novel therapeutic strategies. Concerning the therapeutic evaluation of PLWH, it’s mandatory to evaluate the risk of interactions between cART and other treatments, particularly antiblastic chemotherapy, targeted therapy and immunotherapy. Development of further treatment options for NMSCs in PLWH seems needed. We reviewed the literature after searching for clinical trials, case series, clinical cases and available databases in Embase and Pubmed. We review the incidence of NMSCs among PLWH, focusing our attention on any differences in clinicopathological features of BCC and SCC between PLWH and HIV negative persons, as well as on any differences in efficacy and safety of treatments and response to immunomodulators and finally on any differences in rates of metastatic disease and outcomes.

scholarly journals Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing Techniques

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jaweria Kainat ◽  
Syed Sajid Ullah ◽  
Fahd S. Alharithi ◽  
Roobaea Alroobaea ◽  
Saddam Hussain ◽  
...  
Keyword(s):  
Image Processing ◽  
Feature Fusion ◽  
Principal Component ◽  
Features Extraction ◽  
Support Vector ◽  
Features Fusion ◽  
Extraction Step ◽  
Processing Techniques ◽  
Feature Score

Existing plant leaf disease detection approaches are based on features of extracting algorithms. These algorithms have some limits in feature selection for the diseased portion, but they can be used in conjunction with other image processing methods. Diseases of a plant can be classified from their symptoms. We proposed a cucumber leaf recognition approach, consisting of five steps: preprocessing, normalization, features extraction, features fusion, and classification. Otsu’s thresholding is implemented in preprocessing and Tan–Triggs normalization is applied for normalizing the dataset. During the features extraction step, texture and shape features are extracted. In addition, increasing the instances improves some characteristics. Through a principal component analysis approach, serial feature fusion is employed to provide a feature score. Fused features can be classified through a support vector machine. The accuracy of the Fine KNN is 94.30%, which is higher than the previous work in past papers.

Arrhythmia Classification Based on Multiple Features Fusion and Random Forest Using ECG

2019 ◽  
Vol 9 (8) ◽  
pp. 1645-1654
Author(s):  
Zhizhong Wang ◽  
Hongyi Li ◽  
Chuang Han ◽  
Songwei Wang ◽  
Li Shi
Keyword(s):  
Random Forest ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Principal Component ◽  
Support Vector ◽  
Features Fusion ◽  
Specificity And Sensitivity ◽  
Average Accuracy ◽  
Skewness Coefficient ◽  
Novel Method

Cardiovascular diseases have become more and more prominent in recent years, which have proven to be a major threat to people's health. Accurate detection of arrhythmia in patients has important implications for clinical treatment. The aim of this study was to propose a novel automatic classification method for arrhythmia in order to improve classification accuracy. The electrocardiogram (ECG) signal was subjected preprocessing for denoising purposes using a wavelet transform. Then, the local and global characteristics of the beat, which contained RR interval features according with the clinical diagnosis criterion, morphology features based on wavelet packet decomposition and statistical features along with kurtosis coefficient, skewness coefficient and variance are exploited and fused. Meanwhile, the dimensionality of wavelet packet coefficients were reduced via principal component analysis (PCA). Finally, these features were used as the input of the random forest classifier to train the model and were then compared with the support vector machine (SVM) and back propagation (BP) neural networks. Based on 100,647 beats from the MIT-BIH database, the proposed method achieved an average accuracy, specificity and sensitivity of 99.08%, 99.00% and 89.31%, respectively, using the intra-patient beats, and 92.31%, 89.98% and 37.47%, respectively, using the inter-patient beats. Moreover, two classification schemes, namely, inter-patient and intra-patient scheme, were validated. Compared with the other methods referred to in this paper, the performance of the novel method yielded better results.

Multi-Class Skin Cancer Detection and Classification Using Hybrid Features Extraction Techniques

2020 ◽  
Vol 10 (10) ◽  
pp. 2466-2472
Author(s):  
Mahnoor Masood ◽  
Khalid Iqbal ◽  
Qasim Khan ◽  
Ali Saeed Alowayr ◽  
Khalid Mahmood Awan ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Skin Lesion ◽  
Early Stage ◽  
Noise Removal ◽  
Support Vector ◽  
Hybrid Features ◽  
Cancer Types ◽  
Biopsy Methods ◽  
Skin Cancer Detection ◽  
Aided Diagnosis

Skin cancer is measured as one of the fatal types of cancer diseases in humans, among numerous kinds of malignancy. Current diagnostic classifications are lacking in finding an effective treatment. The effective and early stage treatment of skin disease can increase the survival rate of patients. Substantial investigative work has been developed to improve computer aided diagnosis system to detect cancer at early stage. However, early detection of skin cancer still requires better accuracy through experiment on digital skin lesion images as a multiclass classification, rather than using biopsy methods. This paper presents an intelligent framework to detect and classify four types of skin cancer. Before classification, noise removal from skin lesion is performed by gaussian filter. Textural and colour features are extracted from skin lesion to detect and classify cancer into four types. Support vector Machine is trained to classify Melanoma, Nevus, Basal and Squamous skin cancer types. Extensive experiments are performed on standard benchmark skin cancer images dataset with an improvement in accuracy of 92.41% after comparison with the well-known methods.

GA Optimized Binary Tree SVM for Analog Circuit Fault Diagnosis

2012 ◽  
Vol 235 ◽  
pp. 423-427 ◽  
Author(s):  
Bao Yu Dong ◽  
Guang Ren
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Binary Tree ◽  
Analog Circuit ◽  
Wavelet Packet ◽  
Principal Component ◽  
Monte Carlo Analysis ◽  
Support Vector ◽  
Roulette Wheel Selection ◽  
Circuit Fault Diagnosis

This paper presents a novel method of analog circuit fault diagnosis based on genetic algorithm (GA) optimized binary tree support vector machine (SVM). The real-valued coding genetic algorithm is used to optimize the binary tree structure. In optimization algorithm, we use roulette wheel selection operator, partially mapped crossover operator, inversion mutation operator. In simulation experiment, we use Monte-carlo analysis for 40kHz Sallen-Key bandpass filter and get transient response of ten faults. Then we extract feature vector by db3 wavelet packet transform and principal component analysis (PCA), and diagnose circuit faults by different SVM methods. Experiment results show the proposed method has the better classification accuracy than one-against-one (o-a-o), one-against-rest (o-a-r), Directed Acyclic Graph SVM (DAGSVM) and binary tree SVM (BT-SVM). It is suitable for practical use.

scholarly journals Categorization of Common Pigmented Skin Lesions (CPSL) using Multi-Deep Features and Support Vector Machine

2021 ◽  
Author(s):  
SANTI BEHERA ◽  
PRABIRA SETHY
Keyword(s):  
Support Vector Machine ◽  
Skin Cancer ◽  
Principal Component ◽  
Skin Lesions ◽  
Classification Model ◽  
Healthcare Sector ◽  
Support Vector ◽  
Svm Classifier ◽  
Deep Feature ◽  
Pigmented Skin Lesions

Abstract The skin is the main organ. It is approximately 8 pounds for the average adult. Our skin is a truly wonderful organ. It isolates us and shields our bodies from hazards. However, the skin is also vulnerable to damage and distracted from its original appearance; brown, black, or blue, or combinations of those colors, known as pigmented skin lesions. These common pigmented skin lesions (CPSL) are the leading factor of skin cancer, or can say these are the primary causes of skin cancer. In the healthcare sector, the categorization of CPSL is the main problem because of inaccurate outputs, overfitting, and higher computational costs. Hence, we proposed a classification model based on multi-deep feature and support vector machine (SVM) for the classification of CPSL. The proposed system comprises two phases: first, evaluate the 11 CNN model's performance in the deep feature extraction approach with SVM. Then, concatenate the top performed three CNN model's deep features and with the help of SVM to categorize the CPSL. In the second step, 8192 and 12288 features are obtained by combining binary and triple networks of 4096 features from the top performed CNN model. These features are also given to the SVM classifiers. The SVM results are also evaluated with principal component analysis (PCA) algorithm to the combined feature of 8192 and 12288. The highest results are obtained with 12288 features. The experimentation results, the combination of the deep feature of Alexnet, VGG16 & VGG19, achieved the highest accuracy of 91.7% using SVM classifier. As a result, the results show that the proposed methods are a useful tool for CPSL classification.

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