scholarly journals Design and Implement of Deep Learning Model to Detect the Melanoma

2020 ◽  
Vol 8 (5) ◽  
pp. 3497-3504
Keyword(s):  
Deep Learning ◽  
Skin Cancer ◽  
Clustering Algorithm ◽  
Early Stage ◽  
Skin Lesions ◽  
The Body ◽  
Low Contrast ◽  
Skin Imaging ◽  
Non Melanoma Skin Cancer ◽  
Initial Stage

Detecting Skin lesions on the human body is a big task to the doctors in the initial stage because of the low contrast on the body. This skin cancer can be occur due to sun rays. If the disease cannot detect in early stage, there it may cause death to human lives. Here there are some algorithms to predict the melanoma using deep learning techniques. ISIC International Skin Imaging Collaboration Archive set where it provides various images of melanoma and non-melanoma. There are so many challenges to identify the image with melanoma and non-melanoma types of skin cancer. In this paper we applied hair removal algorithm and k-means clustering algorithm where to remove unwanted substances from the original images. To classify the melanoma and non-melanoma skin cancer, this paper proposed prediction process and sequential CNN architecture.

scholarly journals Automatic Detection and Analysis of Melanoma Skin Cancer using Dermoscopy Images

2019 ◽  
Vol 8 (3) ◽  
pp. 2116-2122
Keyword(s):  
Skin Cancer ◽  
Early Stage ◽  
Skin Lesions ◽  
The Body ◽  
Post Process ◽  
Decision Tree Classification ◽  
Melanoma Diagnosis ◽  
Segmentation Accuracy ◽  
Infected Area ◽  
Melanoma Skin

Skin cancer is known as one of the most risky types of cancer. Several kinds of skin cancer, such as melanoma, basal and squamous cell carcinoma, etc., are available. The most unpredictable cancer is melanoma. If we can detect melanoma skin cancer at an early stage, the chances of recovery will be good and we can save many valuable lives. But if we fail to detect early, melanoma can disperse to the different parts of the body and chance of recovery will become difficult. This research presents a developed system to do melanoma diagnosis by using several dermoscopy images. In this research, we preprocessed the images to remove hairs and noises by using some filter techniques such as dull razor technique, median filtering, etc. After that, we segmented the image to find the infected area using some segmentation method and we choose the method that will give us the best results. Then we post-process the images and choose the most infected lesion. After segmentation of the skin lesion, we checked the segmentation accuracy concerning some basic criteria. We compared the segmented skin lesions with the marked skin lesions by a dermatologist. Then we extracted the features of the images of different criteria, such as Asymmetry, Border irregularity, Color variance, Diameter which have the acronym as ABCD. We also analyzed the texture of the lesions and extracted the geometrical features. Finally, we choose decision tree classification methods that gave us the best results

scholarly journals Screening for Melanoma and Other Skin Cancer Shows a Higher Early Melanoma Incidence: Social Educational Program “Life Fear-Free”

2021 ◽  
Vol 8 (1) ◽  
pp. 54-68
Author(s):  
Lev Demidov ◽  
Igor Samoylenko ◽  
Nina Vand ◽  
Igor Utyashev ◽  
Irina Shubina ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Screening Program ◽  
Excisional Biopsy ◽  
Skin Lesions ◽  
Detection Methods ◽  
Non Melanoma Skin Cancer ◽  
The Social ◽  
Educational Campaign ◽  
Skin Cancer Detection ◽  
The Impact

Background: The screening program Life Fear-Free (LFF) aimed at early diagnosis of cutaneous melanoma (CM) was introduced in Samara, Chelyabinsk, Yekaterinburg, and Krasnodar (Russia) in 2019. Objectives: To analyze the impact of the program on early CM and non-melanoma skin cancer (NMSC) detection. Methods: According to the social educational campaign, people were informed about CM risk factors and symptoms and were invited for skin examination. The program planned to involve 3200 participants in total. Participants with suspicious lesions were invited for excisional biopsy. Results: 3143 participants, including 75.4% women, were examined for skin lesions. The average age of the participants was 43.7 years. Mostly skin phototypes II and III were registered (48.2% and 41.0%, respectively); 3 patients had CM, 15 had basal cell carcinoma, and 1 had Bowen’s disease, which were confirmed histologically. All detected melanomas had Breslow’s thickness of 1 mm. Conclusion: The participants showed high interest in early skin cancer detection programs. The incidence rate of CM and NMSCs among the program participants was higher than in general public. The early disease grade was proven for the detected CMs and NMSCs. The study has shown that it is important to continue such programs.

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 SKIN LESION CLASSIFICATION FROM DERMOSCOPY AND CLINICAL IMAGES WITH A DEEP LEARNING APPROACH

2021 ◽  
Vol 9 (10) ◽  
pp. 1294-1300
Author(s):  
Aigli Korfiati ◽  
◽  
Giorgos Livanos ◽  
Christos Konstandinou ◽  
Sophia Georgiou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Cancer ◽  
Medical Image ◽  
Skin Lesions ◽  
Learning Approaches ◽  
Early Screening ◽  
Clinical Images ◽  
Cad Systems ◽  
Learning Classifier ◽  
Aided Diagnosis

Computer-aided diagnosis (CAD) systems based on deep learning approaches are now feasible due to the availability of big data and the availability of powerful computational resources.The medical image-based CAD systems are of great interest in numerous diseases, but especially for skin cancer diagnosis, deep learning models have been mostly developed for dermoscopy images. Models for clinical images are few, mainly due to the unavailability of big volumes of relevant data. However, CAD systems able to classify skin lesions from clinical images would be of great valueboth for the population and clinicians as an initial early screening of lesions that would leadpatients to visiting a dermatologist in case of suspicious lesions. This is even more pronounced in areas where there is lack of dermoscopy instruments. Thus, in this paper, we aimed to build a classifier based on bothdermoscopy and clinical images able to discriminate skin cancer from skin lesions. The classification is made among three benign and two malignant categories, which include Nevus, Benign but not nevus, Benign but suspicious for malignancy, Melanoma and Non-Melanocytic Carcinoma.The proposed deep learning classifier achieves an Area Under Curve ranging between 0.75 and 0.9 for the five examined categories.

scholarly journals Extensive Lepromatous Lymphadenitis Preceding Lesions on the Face and Earlobes: An Unusual Presentation of Leprosy in Singapore

2018 ◽  
Vol 10 (1) ◽  
pp. 35-40
Author(s):  
Shi Yao Sam Yang ◽  
Wai Mun Sean Leong ◽  
Cruz Maria Teresa Kasunuran ◽  
Jing Xiang Huang ◽  
Sue-Ann Ju Ee Ho ◽  
...  
Keyword(s):  
Early Stage ◽  
Developed Countries ◽  
Skin Lesions ◽  
The Body ◽  
The Other ◽  
Cutaneous Lesions ◽  
Poor Countries ◽  
Geographical Regions ◽  
Resource Poor ◽  
The Face

Leprosy is also known as Hansen disease, as in some countries the diagnosis of leprosy carries a negative stigma and patients fear being shunned as outcasts. Presently, leprosy is primarily limited to specific geographical regions in resource-poor countries. As a result, there is increased difficulty for the younger generation of physicians today to correctly identify leprosy due to a lack of exposure and a low-index of suspicion, particularly in developed countries. In this case, the indurated lesions over the face demonstrated a preference for the outer lateral aspects over the maxillary areas, the nose bridge, and the pinna of the ears consistent with the organism’s preference for cooler regions of the body. This was also evident in the other skin lesions affecting the more acral regions of the limbs in the early stage of disease progression. There is a need to keep this infective condition as an alternate diagnosis to all unusual cutaneous lesions.

scholarly journals Fluorescence detection and diagnosis of non-melanoma skin cancer at an early stage

2009 ◽  
Vol 41 (2) ◽  
pp. 96-103 ◽  
Author(s):  
Jaap de Leeuw ◽  
Nick van der Beek ◽  
W. Dieter Neugebauer ◽  
Peter Bjerring ◽  
H.A. Martino Neumann
Keyword(s):  
Skin Cancer ◽  
Fluorescence Detection ◽  
Early Stage ◽  
Non Melanoma Skin Cancer ◽  
Detection And Diagnosis ◽  
Melanoma Skin

scholarly journals Deep Hybrid Convolutional Neural Network for Segmentation of Melanoma Skin Lesion

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Cheng-Hong Yang ◽  
Jai-Hong Ren ◽  
Hsiu-Chen Huang ◽  
Li-Yeh Chuang ◽  
Po-Yin Chang
Keyword(s):  
Image Segmentation ◽  
Skin Cancer ◽  
Skin Lesion ◽  
Survival Rates ◽  
Skin Lesions ◽  
Manual Segmentation ◽  
Cure Rate ◽  
Dice Coefficient ◽  
Skin Imaging ◽  
Low Efficiency

Melanoma is a type of skin cancer that often leads to poor prognostic responses and survival rates. Melanoma usually develops in the limbs, including in fingers, palms, and the margins of the nails. When melanoma is detected early, surgical treatment may achieve a higher cure rate. The early diagnosis of melanoma depends on the manual segmentation of suspected lesions. However, manual segmentation can lead to problems, including misclassification and low efficiency. Therefore, it is essential to devise a method for automatic image segmentation that overcomes the aforementioned issues. In this study, an improved algorithm is proposed, termed EfficientUNet++, which is developed from the U-Net model. In EfficientUNet++, the pretrained EfficientNet model is added to the UNet++ model to accelerate segmentation process, leading to more reliable and precise results in skin cancer image segmentation. Two skin lesion datasets were used to compare the performance of the proposed EfficientUNet++ algorithm with other common models. In the PH2 dataset, EfficientUNet++ achieved a better Dice coefficient (93% vs. 76%–91%), Intersection over Union (IoU, 96% vs. 74%–95%), and loss value (30% vs. 44%–32%) compared with other models. In the International Skin Imaging Collaboration dataset, EfficientUNet++ obtained a similar Dice coefficient (96% vs. 94%–96%) but a better IoU (94% vs. 89%–93%) and loss value (11% vs. 13%–11%) than other models. In conclusion, the EfficientUNet++ model efficiently detects skin lesions by improving composite coefficients and structurally expanding the size of the convolution network. Moreover, the use of residual units deepens the network to further improve performance.

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 Automated Framework for Segmenting Skin Lesions using Artificial Bee Colony Optimization with Morphological Reconstruction

2019 ◽  
Vol 9 (1S3) ◽  
pp. 144-148
Keyword(s):  
Skin Cancer ◽  
Artificial Bee Colony ◽  
Early Stage ◽  
Signal To Noise Ratio ◽  
Skin Lesions ◽  
Computational Time ◽  
Morphological Reconstruction ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Fuzzy C Means Clustering

Nowadays, Many people are affected by skin cancers. Our proposed work designed a framework to extract the skin cancer using artificial bee colony with morphological reconstruction filters, which helps the demonologist to prevent the severity in early stage, Melanoma is the now become a harmful form of skin cancer which leads the skin cells to grow rapidly and form cancerous tumors. We collected various melanoma images from having used samples from public dataset like ISIC archive and a few from clinical datasets. To remove the noise, median filtering is used for preprocessing in the first step, to segment the tumor boundary Artificial bee colony is used and to remove the unwanted pixels using morphological reconstruction filters. Segmentation metrics like precision, recall, accuracy, Mean Square Error, Peak signal to noise ratio and computational time were calculated. Our proposed method yield 97.7% segmentation accuracy when compared with the level set method and Fuzzy C Means clustering techniques

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