scholarly journals Skin Cancer Detection and Classification using KNN Technique

2021 ◽  
Vol 9 (VI) ◽  
pp. 3520-3527
Author(s):  
Apeksha R Swamy
Keyword(s):  
Skin Cancer ◽  
Cancer Detection ◽  
Nearest Neighbor ◽  
Early Stage ◽  
The Body ◽  
K Nearest Neighbor ◽  
Fast Evaluation ◽  
Efficient Treatment ◽  
Medical Computer ◽  
Skin Cancer Detection

Skin cancer is a major health issue worldwide. Skin cancer detection at an early stage is key for an efficient treatment. Lately, it is popular that, deadly form of skin cancer among the other types of skin cancer is melanoma because it's much more likely to spread to other parts of the body if not identified and treated early. The advanced medical computer vision or medical image processing take part in increasingly significant role in clinical detection of different diseases. Such method provides an automatic image analysis device for an accurate and fast evaluation of the sore. The steps involved in this project are collecting skin cancer images from PH2 database, preprocessing, segmentation using thresholding, feature extraction and then classification using K-Nearest Neighbor technique (KNN). The results show that the achieved classification accuracy is 92.7%, Sensitivity 100% and 84.44% Specificity.

scholarly journals Research on SVM and KNN Classifiers for Skin Cancer Detection

2019 ◽  
Vol 9 (2) ◽  
pp. 4627-4632
Keyword(s):  
Skin Cancer ◽  
Cancer Detection ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Contour Segmentation ◽  
Knn Classifier ◽  
Human Disorder ◽  
Skin Cancer Detection

Generally, a not unusual skin ailment in human disorder. In laptop imaginative and prescient applications, coloration is a sturdy indication for this sickness. This machine identifies pores and skin cancer based totally on the picture of the pores and skin. Initially, the skin image is filtered using filters and segmented Gausian the use of energetic contour segmentation. Segmented pix are fed as an input to the feature extraction. Pictures extracted classified the use of class strategies such as Support Vector Machine classifiers(SVM) and k Nearest Neighbor(kNN) classifiers. SVM classifier provided better results than kNN classifier

scholarly journals Detection and Classification of Melanoma Skin Cancer Analysis

2021 ◽  
pp. 150-154
Author(s):  
Bapu Chendage ◽  
Rajivkumar Mente ◽  
Sunil Pawar
Keyword(s):  
Skin Cancer ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Research Work ◽  
Statistical Features ◽  
K Nearest Neighbor ◽  
Melanoma Cancer ◽  
Knn Classifier ◽  
Melanoma Skin

Nowadays, skin cancer becomes a very dangerous disease for human. Skin cancer is classified into many types such as Melanoma, Basal and squamous cell carcinoma. In all cancers, melanoma is the most dangerous and unpredictable disease. The detection of melanoma cancer in an early stage is beneficial for effective treatment. The detection of skin cancer contains four significant stages which are Pre-processing, Segmentation, Feature Extraction and Classification. The proposed study involves the collection of image database, preprocessing methods, segmentation using thresholding and classification using statistical features. The K-Nearest Neighbor (KNN) classifier is used for classification. The accuracy of KNN classifier for proposed research work is 93.4%.

scholarly journals Implementasi Algoritma Naïve Bayes dan K-Nearest Neighbor Dalam Menentukan Tingkat Keberhasilan Immunotherapy Untuk Pengobatan Penyakit Kanker Kulit

2021 ◽  
Vol 21 (1) ◽  
pp. 259
Author(s):  
F Lia Dwi Cahyanti ◽  
Windu Gata ◽  
Fajar Sarasati
Keyword(s):  
Skin Cancer ◽  
Success Rate ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
The Body ◽  
Ultraviolet Rays ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Process Data

Cancer is a disease that grows in the skin tissue where this condition is characterized by changes in the skin, such as the appearance of lumps, spots, or moles with abnormal sizes, one of the causes of skin cancer is exposure to ultraviolet rays from the sun. One of the treatments for skin cancer is immunotherapy, the immunotherapy method is the treatment of disease by activating or suppressing the immune system in the body. In this study, a comparison with data mining methods for classification was carried out, namely Naïve Bayes and K-Nearest Neighbor to predict the success rate of immunotherapy in curing skin cancer. In the testing process, the researcher uses the Weka application to process data and conduct tests. The results of the tests that have been carried out show that the K-Nearest Neighbor model has the best accuracy value of 91.1111%. while Naïve Bayes obtained a smaller accuracy value, namely 82.2222%. From the test results, it can be concluded that the K-Nearest Neighbor method has better accuracy in determining the success rate of immunotherapy.

Computer-aided Diagnosis of Skin Cancer: A Review

2020 ◽  
Vol 16 (7) ◽  
pp. 781-793 ◽  
Author(s):  
Navid Razmjooy ◽  
Mohsen Ashourian ◽  
Maryam Karimifard ◽  
Vania V. Estrela ◽  
Hermes J. Loschi ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Cancer Detection ◽  
Circulatory System ◽  
The Body ◽  
Pigment Cells ◽  
Health Issues ◽  
Melanoma Cancer ◽  
Skin Cancers ◽  
The World ◽  
Skin Cancer Detection

Cancer is currently one of the main health issues in the world. Among different varieties of cancers, skin cancer is the most common cancer in the world and accounts for 75% of the world's cancer. Indeed, skin cancer involves abnormal changes in the outer layer of the skin. Although most people with skin cancer recover, it is one of the major concerns of people due to its high prevalence. Most types of skin cancers grow only locally and invade adjacent tissues, but some of them, especially melanoma (cancer of the pigment cells), which is the rarest type of skin cancer, may spread through the circulatory system or lymphatic system and reach the farthest points of the body. Many papers have been reviewed about the application of image processing in cancer detection. In this paper, the automatic skin cancer detection and also different steps of such a process have been discussed based on the implantation capabilities.

Melanoma Skin Cancer Detection based on Image Processing

2020 ◽  
Vol 16 (1) ◽  
pp. 50-58 ◽  
Author(s):  
Nadia Smaoui Zghal ◽  
Nabil Derbel
Keyword(s):  
Skin Cancer ◽  
Cancer Detection ◽  
Early Stage ◽  
Skin Lesions ◽  
Simple Method ◽  
Common Cause ◽  
Extraction Stage ◽  
Skin Cancer Detection ◽  
Aided Diagnosis ◽  
Melanoma Skin

Background: Skin cancer is one of the most common forms of cancers among humans. It can be classified as non-melanoma and melanoma. Although melanomas are less common than non-melanomas, the former is the most common cause of mortality. Therefore, it becomes necessary to develop a Computer-aided Diagnosis (CAD) aiming to detect this kind of lesion and enable the diagnosis of the disease at an early stage in order to augment the patient’s survival likelihood. Aims: This paper aims to develop a simple method capable of detecting and classifying skin lesions using dermoscopy images based on ABCD rules. Methods: The proposed approach follows four steps. 1) The preprocessing stage consists of filtering and contrast enhancing algorithms. 2) The segmentation stage aims at detecting the lesion. 3) The feature extraction stage based on the calculation of the four parameters which are asymmetry, border irregularity, color and diameter. 4) The classification stage based on the summation of the four extracted parameters multiplied by their weights yields the total dermoscopy value (TDV); hence, the lesion is classified into benign, suspicious or malignant. The proposed approach is implemented in the MATLAB environment and the experiment is based on PH2 database containing suspicious melanoma skin cancer. Results and Conclusion: Based on the experiment, the accuracy of the developed approach is 90%, which reflects its reliability.

scholarly journals Efficient Outcomes by a New Technique SIMBO-FDTD for Skin Cancer Detection

2021 ◽  
Author(s):  
Shelendra Pal ◽  
Rajkumar ◽  
Vipul Sharma
Keyword(s):  
Skin Cancer ◽  
Cancer Detection ◽  
Imaging Techniques ◽  
Research Paper ◽  
The Body ◽  
Classification Problems ◽  
Correct Treatment ◽  
Detection Techniques ◽  
A New Technique ◽  
Skin Cancer Detection

Abstract Cancer is one of the most common diseases affecting humans globally of which skin cancer is one. Formerly, medical imaging techniques are designed to provide physicians with information about the body and out-of-body diseases and for their intended diagnosis. By identifying and treating the exact location of a patient's cancer cells, the patient's chances of survival greatly increase and are currently used for various classification problems. However, to achieve accurate and simple future performance, yet to be achieved and Correct treatment based on its accurate prognosis will be an important step in the evaluation of disease outcomes. At this time, freezing, axial surgery, MOH surgery, curettage, and electro dissection or cryotherapy, radiation therapy, chemotherapy, photodynamic therapy, biological therapy are several corrective tools for its treatment. The main objective of this research paper is the development of skin cancer detection techniques using finite difference time domain(FDTD) using various computer image techniques and the research paper suggests that the technique being designed has the primary ability to better diagnose skin cancer than any other current investigator.

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