Skin Lesions Detection using Meta-Heuristic Method (original) (raw)
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Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm
Symmetry, 2018
The occurrence rates of melanoma are rising rapidly, which are resulting in higher death rates. However, if the melanoma is diagnosed in Phase I, the survival rates increase. The segmentation of the melanoma is one of the largest tasks to undertake and achieve when considering both beneath and over the segmentation. In this work, a new approach based on the artificial bee colony (ABC) algorithm is proposed for the detection of melanoma from digital images. This method is simple, fast, flexible, and requires fewer parameters compared with other algorithms. The proposed approach is applied on the PH2, ISBI 2016 challenge, the ISBI 2017 challenge, and Dermis datasets. These bases contained images are affected by different abnormalities. The formation of the databases consists of images collected from different sources; they are bases with different types of resolution, lighting, etc., so in the first step, the noise was removed from the images by using morphological filtering. In the n...
2020
In recent years, the incidence of skin lesions has been one of the most rapidly increasing of all commonly occurring cancers. This deadliest form of melanoma must be detected early to be effectively treated. Because of the trouble and objectivity of human clarification, a significant research field has developed around the computerized examination of dermoscopy images. One reason to apply swarm intelligence systems is that an optimal solution can be advanced with a sensible computational application. This work introduces an artificial bee colony technique (ABC), distinctions, and applications. The planned ABC is a more suitable algorithm and one that requires smaller amounts of factors that need to be adjusted in comparison to other modern artificial swarm intelligence techniques (MASITs) for distinguishing unhealthy in skin tumor lesions. In these swarm's intelligence optimization algorithms have been positively executed for melanoma problems and provided extraordinary results ...
Discrimination between Healthy and Unhealthy Mole Lesions using Artificial Swarm Intelligence
IOP Conference Series: Materials Science and Engineering
In recent years, occurrence rates of skin melanoma have shown a rapid increase, resulting in enhancements to death rates. Based on the difficulty and subjectivity of human clarification, computer examination of dermoscopy images has thus developed into a significant research field in this area. One the reasons for applying heuristic methods is that good solutions can be developed with only reasonable computational exertion. This paper thus presents an artificial swarm intelligence method with variations and suggestions. The proposed artificial bee colony (ABC) is a more suitable algorithm in comparison to other algorithms for detecting melanoma in the skin tumour lesions, being flexible, fast, and simple, and requiring fewer adjustments. These is characteristics are recognized assisting dermatologists to detect malignant melanoma (MM) at the lowest time and effort cost. Automatic classification of skin cancers by using segmenting the lesion's regions and selecting of the ABC technique for the values of the characteristic principles allows. Information to be fed into several well-known algorithms to obtain skin cancer categorization: in terms of whether the lesion is suspicious, malignant, benign (healthy and unhealthy nevi). This segmentation approach can further be utilized to develop handling and preventive approaches, thus decreasing the danger of skin cancer lesions. One of the most significant stages in dermoscopy image examination is the segmentation of the melanoma. Here, various PH2 dataset image were utilized along with their masks to estimate the accuracy, sensitivity, and specificity of various segmentation techniques. The results show that a modified automatic based on ABC images have the highest accuracy and specificity compares with the other algorithms. The results show that a modified automatic based on ABC images displayed the highest accuracy and specificity in such testing.
Melanoma Detection From Lesion Images Using Optimized Features Selected by Metaheuristic Algorithms
International Journal of Healthcare Information Systems and Informatics, 2021
This paper deals with a simple but efficient method for detection of deadly malignant melanoma with optimized hand-crafted feature sets selected by three alternative metaheuristic algorithms, namely Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Simulated Annealing (SA). Total 1898 number of features relating to lesion shapes, colors and textures are extracted from each of the 170 non-dermoscopy camera images of the popular MED-NODE dataset. This large feature set is then optimized and the number of features is reduced to up-to the range of single digit using metaheuristic algorithms as feature selector. Two well-known supervised classifiers, i.e. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used to classify malignant and benign lesions. The best classification accuracy result found by this method is 87.69% with only 7 features selected by PSO using ANN classifier which is far better than the results found in the literature so far.
An efficient of estimation stages for segmentation skin lesions based optimization algorithm
International Journal of Electrical and Computer Engineering (IJECE), 2021
Modern dermatology distinguishes premature diagnosis for example an important part in reducing the death percentage and promising less aggressive treatment for patients. The classifications comprise various stages that must be selected suitably using the characteristics of the filter pointing to get a dependable analysis. The dermoscopic images hold challenges to be faced and overcome to enhance the automatic diagnosis of hazardous lesions. It is calculated to survey a different metaheuristic and evolutionary computing working for filter design systems. Approximately general computing techniques are observed to improve features of infect design method. Nevertheless, the median filter (MF) is normally multimodal with respect to the filter factors and so, reliable approaches that can provide optimal solutions are required. The design of MF depends on modern artificial swarm intelligence technique (MASIT) optimization algorithm which has proven to be more effective than other population-based algorithms to improve of estimation stages for segmentation skin lesions. A controlled artificial bee colony (ABC) algorithm is advanced for solving factors optimization problems and, also the physical-programming-depend on ABC way is applied to proposal median filter, and the outcomes are compared to another approaches.
IOP Conference Series: Materials Science and Engineering
Over recent decades, updates in the field of the imaging of skin tumor lesions and biomedical sensing at the side of its related clinical implementations have attracted the attention of scientists. The major aim is to develop an artificial bee colony algorithmic and other approaches to propose different types of skin cancer lesion medical imaging, utilizing medical images achieved through different imaging methods. The artificial bee colony is frequently used to get highly accurate analysis, which supports the early detection of that which poses a threat to life, and, this method can be a good area to examine for future research. We compared the achievement of many works that were developed specially to detect the skin tumours lesion of healthy and unhealthy nevi after the use of methods and algorithms to obtain good processing results and discussed the relevant conclusions. The paper presents develop various classification, segmentation, methods and algorithms to recognize benign and malignant nevi to help make an expert decision on dermatology. These methods would allow not just earlier detection of skin cancer cases, however, decreasing of the number of unnecessary of steps in image processing and costly process.
International Journal of Electrical and Computer Engineering (IJECE), 2020
Methods of image processing can recognize the images of melanoma lesions border in addition to the disease compared to a skilled dermatologist. New swarm intelligence technique depends on meta-heuristic that is industrialized to resolve composite real problems which are problematic to explain by the available deterministic approaches. For an accurate detection of all segmentation and classification of skin lesions, some dealings should be measured which contain, contrast broadening, irregularity quantity, choice of most optimal features, and so into the world. The price essential for the action of progressive disease cases is identical high and the survival percentage is low. Many electronic dermoscopy classifications are advanced depend on the grouping of form, surface and dye features to facilitate premature analysis of malignance. To overcome this problematic, an effective prototypical for accurate boundary detection and arrangement is obtainable. The projected classical recovers...
Segmentation and classification of melanoma and benign skin lesions
Optik, 2017
The incidence ofmalignant melanoma has been increasing worldwide. An efficient non-invasive computer-aided diagnosis (CAD) is seen as a solution to make identification process faster, and accessible to a large population. Such automated system relies on three things: reliable lesion segmentation, pertinent features' extraction and good lesion classifier. In this paper, we propose an automated system that uses an Ant colony based segmentation algorithm, takes into consideration three types of features to describe malignant lesion:geometrical properties, textureand relative colors from which pertinent ones are selected, and uses two classifiers K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN). The objective of this paper is to test the efficiency of the proposed segmentation algorithm, extract most pertinent features that describe melanomas and compare the two classifiers. Our automated system is tested on 172 dermoscopic images where 88 are malignant melanomas and 84 benign lesions. The results of the proposed segmentation algorithm are encouraging as they gave promising results. 12 features seem to be sufficient to detect malignant melanoma. Moreover, ANN gives better results than KNN.
Soft Computing Approach Based Melanoma Detection Techniques: A Review
Ethics and Information Technology, 2020
Skin disease is the world's fourth largest cause of non-fatal diseases impacting 30-70% of populations and it is widespread across geographies and ages. In the United States, skin disease is the primary form of skin cancer. Skin disease is one of the most prevalent health issues. Over the years hepatic melanoma diagnosis is a complex field of research. Nevertheless, dermatologists usually have low supplies and the consultation costs are high, particularly in rural areas. Dermatoscopy is a widely used diagnostic method that enhances the detection of skin lesions, as it provides important lesion information that can be useful for the clinician and an automated alert tool. Diagnostic systems operated by computers need sophisticated image processing algorithms to provide statistical representations of suspended areas. In this article we have analysed the current technological advancements used in computer-aided skin lesion diagnostic techniques. The stages include the pre-processing of dermoscopic images, segmenting, feature selection and classification of specific characteristics and relegation of skin lesions.
IOP Conference Series: Materials Science and Engineering
One of the damaging diseases among people in the world is skin cancer. Skin cancer leftovers an important scientific, clinical and public task. Swarm intelligence techniques (SITs) are new, improved and modern methods for optimization algorithms. Failure of detection in skin cancer images can be seen in SITs. This work proposes an efficient image and examines for some samples in this disease. The study presents a simple technique for a pre-processing and an automatic detection of SITs to make the needed analysis. This paper estimated all these various models using the PH 2 , Dermis, ISIC (2016 ,2017, 2018) segmentation challenge dataset. The input images are improved for better processing than, the lesion sampling is segmented from the improved image by using Otsu thresholding and median filter operations. In the earlier studies, skin cancer is analyzed by means of several optimization algorithms. Now, the outcomes of the above algorithms were compared with the dice coefficient and it was demonstrated that the value of 97.35% which is nearer to manual segmentation. The accuracy the value of 98.58% when used for solving the same problem. To this end, a somewhat comprehensive analysis was showed to compare the effectiveness of many parameters' combinations.