scholarly journals Design of a tool for the classification of skin cancer images using Deep Neural Networks (DNN)

2021 ◽  
pp. 65-80
Author(s):  
Diana Paola Merchán Vargas ◽  
Helis Navarro Báez ◽  
Jaime Guillermo Barrero Pérez ◽  
Jeyson Arley Castillo Bohórquez
Keyword(s):  
Skin Cancer ◽  
Embedded System ◽  
Image Classification ◽  
Life Quality ◽  
Skin Lesions ◽  
Raspberry Pi ◽  
World Population ◽  
Advanced Stages ◽  
Reliable Solution

Skin cancer is one of the most common diseases in the world population. Usually, the diagnosis requires the acquisition of dermatoscopic images. Both biopsy and histopathology have been used in advanced stages. Its early detection is very important to increase patient life quality and life expectancy. In Colombia, the lack of qualified professionals and medical instruments difficulties this task. The automatic classification is a huge challenge, due to ample variety and morphology in skin lesions. Nowadays, Deep Learning reaches elevated accuracy levels in image classification tasks and is set to become a reliable solution for medical image classification. In this research, used these DNN advantages to build a convolutional neural network (CNN) trained with open source databases to the classification of skin lesions benign and malignant. After the training process, we develop an embedded system with raspberry Pi 3 B+ with a generic camera and implemented the CNN described in Python coded-based. For the benign and malignant classification, the prototype reached an accuracy level of 91.06% in the F1 score and a recall of 91.98%.

scholarly journals Classification of Dermoscopic Image of Skin Cancer Using the GLCM Method and Multi-SVM Algorithm

Rekayasa ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 407-415
Author(s):  
Riyan Latifahul Hasanah ◽  
Dwiza Riana
Keyword(s):  
Skin Cancer ◽  
Correlation Energy ◽  
Texture Feature ◽  
Skin Lesions ◽  
Pigment Cells ◽  
Binary Images ◽  
Svm Algorithm ◽  
Dermoscopic Image ◽  
Processing Techniques

The development of abnormal skin pigment cells can cause a skin cancer called melanoma. Melanoma can be cured if diagnosed and treated in its early stages. Various studies using various technologies have been developed to conduct early detection of melanoma. This research was conducted to diagnose melanoma skin cancer with digital image processing techniques on the dermoscopic image of skin cancer. The diagnosis is made by classifying dermoscopic images based on the types of Common Nevus, Atypical Nevus or Melanoma. Pre-processing is done by changing the RGB image to grayscale (grayscaling), smoothing image using median filtering, and image segmentation based on binary images of skin lesions. The value of Contrast, Correlation, Energy and Homogeneity obtained from the texture feature extraction of the GLCM method is used in the next step, which is the classification process with the Multi-SVM algorithm. The proposed research method shows high accuracy results in diagnosing skin cancer

scholarly journals Computer-Aided Diagnosis of Micro-Malignant Melanoma Lesions Applying Support Vector Machines

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Joanna Jaworek-Korjakowska
Keyword(s):  
Skin Cancer ◽  
Malignant Melanoma ◽  
Early Stage ◽  
Skin Lesions ◽  
Support Vector ◽  
Svm Classifier ◽  
Stage Of Development ◽  
Vector Machines ◽  
Aided Diagnosis

Background. One of the fatal disorders causing death is malignant melanoma, the deadliest form of skin cancer. The aim of the modern dermatology is the early detection of skin cancer, which usually results in reducing the mortality rate and less extensive treatment. This paper presents a study on classification of melanoma in the early stage of development using SVMs as a useful technique for data classification.Method. In this paper an automatic algorithm for the classification of melanomas in their early stage, with a diameter under 5 mm, has been presented. The system contains the following steps: image enhancement, lesion segmentation, feature calculation and selection, and classification stage using SVMs.Results. The algorithm has been tested on 200 images including 70 melanomas and 130 benign lesions. The SVM classifier achieved sensitivity of 90% and specificity of 96%. The results indicate that the proposed approach captured most of the malignant cases and could provide reliable information for effective skin mole examination.Conclusions. Micro-melanomas due to the small size and low advancement of development create enormous difficulties during the diagnosis even for experts. The use of advanced equipment and sophisticated computer systems can help in the early diagnosis of skin lesions.

scholarly journals A deep learning approach based hardware solution to categorise garbage in environment

2021 ◽  
Author(s):  
Tanya Gupta ◽  
Rakshit Joshi ◽  
Devarshi Mukhopadhyay ◽  
Kartik Sachdeva ◽  
Nikita Jain ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Embedded System ◽  
Image Classification ◽  
Urban Areas ◽  
Performance Measure ◽  
Raspberry Pi ◽  
Infrared Sensors ◽  
Base Level ◽  
Neural Network System

AbstractGarbage detection and disposal have become one of the major hassles in urban planning. Due to population influx in urban areas, the rate of garbage generation has increased exponentially along with garbage diversity. In this paper, we propose a hardware solution for garbage segregation at the base level based on deep learning architecture. The proposed deep-learning-based hardware solution SmartBin can segregate the garbage into biodegradable and non-biodegradable using Image classification through a Convolutional Neural Network System Architecture using a Real-time embedded system. Garbage detection via image classification aims for quick and efficient categorization of garbage present in the bin. However, this is an arduous task as garbage can be of any dimension, object, color, texture, unlike object detection of a particular entity where images of objects of that entity do share some similar characteristics and traits. The objective of this work is to compare the performance of various pre-trained Convolution Neural Network namely AlexNet, ResNet, VGG-16, and InceptionNet for garbage classification and test their working along with hardware components (PiCam, raspberry pi, infrared sensors, etc.) used for garbage detection in the bin. The InceptionNet Neural Network showed the best performance measure for the proposed model with an accuracy of 98.15% and a loss of 0.10 for the training set while it was 96.23% and 0.13 for the validation set.

scholarly journals Image classification of primary morphological elements of skin lesions

2019 ◽  
Vol 19 (2) ◽  
pp. 333-338
Author(s):  
T.A. Polevaya ◽  
I.A. Saitov ◽  
R.A. Ravodin ◽  
A.A. Filchenkov
Keyword(s):  
Image Classification ◽  
Skin Lesions

scholarly journals An algorithm for characterizing skin moles using image processing and machine learning

2021 ◽  
Vol 11 (4) ◽  
pp. 3539
Author(s):  
Zaid Sanchez ◽  
Alicia Alva ◽  
Mirko Zimic ◽  
Christian del Carpio
Keyword(s):  
Machine Learning ◽  
Skin Cancer ◽  
Low Income ◽  
Support Vector ◽  
Classification Algorithms ◽  
Extraction Algorithm ◽  
High Incidence ◽  
Advanced Stages ◽  
Visual Characteristics

<a name="_Hlk65806330"></a><span>Melanoma, the most serious type of skin cancer, forms in cells (melanocytes) that produce melanin, the pigment that gives color to the skin. There are low-income regions that lack specialized dermatologists, causing skin cancer to be diagnosed in advanced stages. In Peru, in high Andean communities with low resources, the problem is aggravated by the high incidence of ultraviolet radiation and lack of medical resources to make the diagnosis. Normally, mole images are obtained from dermatoscopes. The present work seeks to use mole images obtained from smartphones to make the classification of them as suspected or not suspected of being melanoma, by means of a feature extraction algorithm. The first step is to make color and lighting corrections. After this, the image is segmented using the K-Means algorithm, and we obtain the areas of the mole and skin. With the segmented mole we proceed to extract the main visual characteristics and then use classification algorithms such as support vector machine (SVM), random forest and naïve bayes, which obtained an accuracy of 0.9473, 0.7368 and 0.6842, respectively. These results show that it is possible to use images obtained from smartphones to develop a classification algorithm with 94.73% accuracy to detect melanoma in skin moles.</span>

scholarly journals Skin Cancer Diseases Classification using Deep Convolutional Neural Network with Transfer Learning Model

2021 ◽  
Vol 2128 (1) ◽  
pp. 012013
Author(s):  
Laila Moataz ◽  
Gouda I. Salama ◽  
Mohamed H. Abd Elazeem
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Classification Accuracy ◽  
Skin Lesions ◽  
Fine Tuning ◽  
Deep Convolutional Neural Networks ◽  
Proposed Model ◽  
Prior Models ◽  
Cancer Diseases

Abstract Skin cancer is becoming increasingly common. Fortunately, early discovery can greatly improve the odds of a patient being healed. Many Artificial Intelligence based approaches to classify skin lesions have recently been proposed. but these approaches suffer from limited classification accuracy. Deep convolutional neural networks show potential for better classification of cancer lesions. This paper presents a fine-tuning on Xception pretrained model for classification of skin lesions by adding a group of layers after the basic ones of the Xception model and all model weights are set to be trained. The model is fine-tuned over HAM10,000 dataset seven classes by augmentation approach to mitigate the data imbalance effect and conducted a comparative study with the most up to date approaches. In comparison to prior models, the results indicate that the proposed model is both efficient and reliable.

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