scholarly journals Non-Invasive Skin Cancer Diagnosis Using Hyperspectral Imaging for In-Situ Clinical Support

2020 ◽  
Vol 9 (6) ◽  
pp. 1662 ◽  
Author(s):  
Raquel Leon ◽  
Beatriz Martinez-Vega ◽  
Himar Fabelo ◽  
Samuel Ortega ◽  
Veronica Melian ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Cancer Diagnosis ◽  
Skin Lesions ◽  
Morphological Processing ◽  
Automatic Identification ◽  
Routine Practice ◽  
Non Invasive ◽  
Spectral Bands ◽  
Clinical Support

Skin cancer is one of the most common forms of cancer worldwide and its early detection its key to achieve an effective treatment of the lesion. Commonly, skin cancer diagnosis is based on dermatologist expertise and pathological assessment of biopsies. Although there are diagnosis aid systems based on morphological processing algorithms using conventional imaging, currently, these systems have reached their limit and are not able to outperform dermatologists. In this sense, hyperspectral (HS) imaging (HSI) arises as a new non-invasive technology able to facilitate the detection and classification of pigmented skin lesions (PSLs), employing the spectral properties of the captured sample within and beyond the human eye capabilities. This paper presents a research carried out to develop a dermatological acquisition system based on HSI, employing 125 spectral bands captured between 450 and 950 nm. A database composed of 76 HS PSL images from 61 patients was obtained and labeled and classified into benign and malignant classes. A processing framework is proposed for the automatic identification and classification of the PSL based on a combination of unsupervised and supervised algorithms. Sensitivity and specificity results of 87.5% and 100%, respectively, were obtained in the discrimination of malignant and benign PSLs. This preliminary study demonstrates, as a proof-of-concept, the potential of HSI technology to assist dermatologists in the discrimination of benign and malignant PSLs during clinical routine practice using a real-time and non-invasive hand-held device.

Deep Learning-Based Classification of Malignant and Benign Cells in Dermatoscopic Images via Transfer Learning Approach

2021 ◽  
Author(s):  
V. Akash Kumar ◽  
Vijaya Mishra ◽  
Monika Arora
Keyword(s):  
Skin Cancer ◽  
Transfer Learning ◽  
Human Life ◽  
Skin Pigmentation ◽  
Automatic Identification ◽  
Learning Approach ◽  
Predicted Model ◽  
Classification Tasks ◽  
Cancerous Cells

The inhibition of healthy cells creating improper controlling process of the human body system indicates the occurrence of growth of cancerous cells. The cluster of such cells leads to the development of tumor. The observation of this type of abnormal skin pigmentation is done using an effective tool called Dermoscopy. However, these dermatoscopic images possess a great challenge for diagnosis. Considering the characteristics of dermatoscopic images, transfer learning is an appropriate approach of automatically classifying the images based on the respective categories. An automatic identification of skin cancer not only saves human life but also helps in detecting its growth at an earlier stage which saves medical practitioner’s effort and time. A newly predicted model has been proposed for classifying the skin cancer as benign or malignant by DCNN with transfer learning and its pre-trained models such as VGG 16, VGG 19, ResNet 50, ResNet 101, and Inception V3. The proposed methodology aims at examining the efficiency of pre-trained models and transfer learning approach for the classification tasks and opens new dimensions of research in the field of medicines using imaging technique which can be implementable in real-time applications.

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 Skin cancer detection using non-invasive techniques

RSC Advances ◽  
2018 ◽  
Vol 8 (49) ◽  
pp. 28095-28130 ◽  
Author(s):  
Vigneswaran Narayanamurthy ◽  
P. Padmapriya ◽  
A. Noorasafrin ◽  
B. Pooja ◽  
K. Hema ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Cancer Detection ◽  
Cancer Diagnosis ◽  
Non Invasive ◽  
Recent Advances ◽  
Invasive Techniques ◽  
Skin Cancer Detection

Recent advances in non-invasive techniques for skin cancer diagnosis.

scholarly journals Detection theory for accurate and non-invasive skin cancer diagnosis using dynamic thermal imaging

2017 ◽  
Vol 8 (4) ◽  
pp. 2301 ◽  
Author(s):  
Sebastián E. Godoy ◽  
Majeed M. Hayat ◽  
David A. Ramirez ◽  
Stephen A. Myers ◽  
R. Steven Padilla ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Cancer Diagnosis ◽  
Thermal Imaging ◽  
Detection Theory ◽  
Non Invasive

scholarly journals Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 252
Author(s):  
Laura Rey-Barroso ◽  
Sara Peña-Gutiérrez ◽  
Carlos Yáñez ◽  
Francisco J. Burgos-Fernández ◽  
Meritxell Vilaseca ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Cancer Diagnosis ◽  
Multispectral Imaging ◽  
Three Dimensional ◽  
Machine Learning Algorithms ◽  
Non Invasive ◽  
Aggressive Form ◽  
Incidence And Mortality ◽  
Rising Incidence ◽  
Large Research

The worldwide incidence of skin cancer has risen rapidly in the last decades, becoming one in three cancers nowadays. Currently, a person has a 4% chance of developing melanoma, the most aggressive form of skin cancer, which causes the greatest number of deaths. In the context of increasing incidence and mortality, skin cancer bears a heavy health and economic burden. Nevertheless, the 5-year survival rate for people with skin cancer significantly improves if the disease is detected and treated early. Accordingly, large research efforts have been devoted to achieve early detection and better understanding of the disease, with the aim of reversing the progressive trend of rising incidence and mortality, especially regarding melanoma. This paper reviews a variety of the optical modalities that have been used in the last years in order to improve non-invasive diagnosis of skin cancer, including confocal microscopy, multispectral imaging, three-dimensional topography, optical coherence tomography, polarimetry, self-mixing interferometry, and machine learning algorithms. The basics of each of these technologies together with the most relevant achievements obtained are described, as well as some of the obstacles still to be resolved and milestones to be met.

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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