Comparing the capabilities of transfer learning models to detect skin lesion in humans

2020 ◽  
Vol 234 (10) ◽  
pp. 1083-1093 ◽  
Author(s):  
Aditi Singhal ◽  
Ramesht Shukla ◽  
Pavan Kumar Kankar ◽  
Saurabh Dubey ◽  
Sukhjeet Singh ◽  
...  
Keyword(s):  
Skin Lesion ◽  
Transfer Learning ◽  
Wiener Filter ◽  
Skin Lesions ◽  
Automated Classification ◽  
Convolutional Networks ◽  
Image Quantization ◽  
Malignant Skin ◽  
Effective Diagnosis

Effective diagnosis of skin tumours mainly relies on the analysis of the characteristics of the lesion. Automatic detection of malignant skin lesion has become a mandatory task to reduce the risk of human deaths and increase their survival. This article proposes a study of skin lesion classification using transfer learning approach. The transfer learning model uses four different state-of-the-art architectures, namely Inception v3, Residual Networks (ResNet 50), Dense Convolutional Networks (DenseNet 201) and Inception Residual Networks (Inception ResNet v2). These models are trained under the dataset comprising seven different classes of skin lesions. The skin lesion images are pre-processed using image quantization, grayscaling and the Wiener filter before final training step. These models are compared for performance evaluation on different metrics. The present study shows the efficacy of the methodology for automated classification of lesion images.

scholarly journals A Mobile-Based Prototype for Skin Lesion Analysis: Towards a Patient-Oriented Design Approach

2013 ◽  
Vol 9 (S8) ◽  
pp. 27 ◽  
Author(s):  
Luís Rosado ◽  
Maria João Vasconcelos ◽  
Márcia Ferreira
Keyword(s):  
Skin Lesion ◽  
Active Role ◽  
Skin Lesions ◽  
Self Monitoring ◽  
Promising Tool ◽  
Processing Power ◽  
Skin Cancers ◽  
Lesion Analysis ◽  
New Generation

The wide spreading of the new generation of smartphones, with significant improvements in terms of image acquisition and processing power, is opening up the possibility of new approaches for skin lesion monitoring. Mobile Teledermatology appears nowadays as a promising tool with the potential to empower patients to adopt an active role in managing their own skin health status, while facilitates the early diagnosis of skin cancers. The main objective of this work is to create a mobile-based prototype for skin lesions analysis with patient-oriented features and functionalities. The presented self-monitoring system collects, processes and storages information of skin lesions through the automatic extraction and classification of specific visual features. The algorithms used to extract and classify these features are briefly described, as well as the overall system architecture and functionalities.

scholarly journals Classification of skin lesions using transfer learning and augmentation with Alex-net

PLoS ONE ◽  
2019 ◽  
Vol 14 (5) ◽  
pp. e0217293 ◽  
Author(s):  
Khalid M. Hosny ◽  
Mohamed A. Kassem ◽  
Mohamed M. Foaud
Keyword(s):  
Transfer Learning ◽  
Skin Lesions

Detection and Classification of Skin Lesions using Probability Map-based Region Growing with BA-KNN Classifier (Preprint)

2021 ◽  
Author(s):  
Sivaraj S ◽  
Dr.R. Malmathanraj
Keyword(s):  
Skin Lesion ◽  
Region Growing ◽  
Skin Lesions ◽  
Classification Approach ◽  
Probability Map ◽  
Knn Classifier ◽  
Knn Classification ◽  
Pigmented Skin Lesions ◽  
Thresholding Algorithm

BACKGROUND Melanoma is one of the most hazardous existing diseases, and is a kind of threatening pigmented skin lesion. Appropriate automated diagnosis of skin lesions and the categorization of melanoma may be exceptionally enhancing premature identification of melanomas. OBJECTIVE However, Models of categorization based on deterministic skin lesion may influence multi-dimensional nonlinear problem provokes inaccurate and ineffective categorization. This research presents a novel hybrid BA-KNN classification approach for pigmented skin lesions in dermoscopy images. METHODS In the first step, the skin lesion is preprocessed via automatic preprocessing algorithm together with a fusion hair detection and removal strategy. Also, a new probability map based region growing and optimal thresholding algorithm is integrated in this system to enhance the rate of accuracy. RESULTS Moreover, to attain better efficacy, an estimate of ABCD as well as geometric features are considered during the feature extraction to describe the malignancy of the lesion. CONCLUSIONS The evaluation of the experiment reveals the efficiency of the proposed approach on dermoscopy images with better accuracy

scholarly journals Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Joanna Jaworek-Korjakowska ◽  
Paweł Kłeczek
Keyword(s):  
Skin Lesion ◽  
Skin Lesions ◽  
New Approach ◽  
Melanoma Detection ◽  
Specificity And Sensitivity ◽  
Melanocytic Lesions ◽  
Feature Extraction And Selection ◽  
Aided Diagnosis ◽  
Automatic Algorithm

Background. Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis (CAD) systems have been proposed to increase the specificity and sensitivity of melanoma detection. Although such computer programs are developed for different diagnostic algorithms, to the best of our knowledge, a system to classify different melanocytic lesions has not been proposed yet.Method. In this research we present a new approach to the classification of melanocytic lesions. This work is focused not only on categorization of skin lesions as benign or malignant but also on specifying the exact type of a skin lesion including melanoma, Clark nevus, Spitz/Reed nevus, and blue nevus. The proposed automatic algorithm contains the following steps: image enhancement, lesion segmentation, feature extraction, and selection as well as classification.Results. The algorithm has been tested on 300 dermoscopic images and achieved accuracy of 92% indicating that the proposed approach classified most of the melanocytic lesions correctly.Conclusions. A proposed system can not only help to precisely diagnose the type of the skin mole but also decrease the amount of biopsies and reduce the morbidity related to skin lesion excision.

scholarly journals Skin cancer high-risk patient screening from dermoscopic images via Artificial Intelligence: an online study

2021 ◽  
Author(s):  
David Coronado-Gutiérrez ◽  
Carlos López ◽  
Xavier P. Burgos-Artizzu
Keyword(s):  
Artificial Intelligence ◽  
High Risk ◽  
Skin Lesion ◽  
High Risk Patient ◽  
Area Under The Curve ◽  
Skin Lesions ◽  
Risk Patient ◽  
Online Study ◽  
Patient Screening ◽  
Malignant Skin

ABSTRACTObjectivesTo evaluate a novel Artificial Intelligence (AI) method for the detection of malignant skin lesions from dermoscopic images.Methods58,457 dermoscopic images available online from the International Skin Imaging Collaboration (ISIC) Archive were downloaded. These images were acquired from different centers worldwide by recognized dermatologists and show varied clinical outcomes belonging to different types of benign and malign skin lesions. A state-of-the-art AI skin lesion classifier based on Deep Learning was designed. The method, fully automated, first locates and segments the nevus in the image and then classifies it into either benign or malign type.Results1,631 images (2.8%) were discarded due to bad quality. A total of 56,826 images were finally used. Two thirds of the images (37,688) were used to train the classifier, leaving the remaining 19,138 images for validation. In this set, malignant lesions had a prevalence of 15.4% (2,956/19,138). The AI skin lesion classifier reached an area under the curve (AUC) of 87.4%. Optimal cut-off point in terms of accuracy resulted in an 85.9% accuracy (16,439/19,138) and sensibility of 89.6% (2,648/ 2,956) at 85.2% (13,791/16,182) specificity. Negative predictive value (NPV) was 97.8% (13,791/14,099). Other training/validation splits were also evaluated, showing similar results.ConclusionsA novel AI method showed promising results as skin lesion classifier from dermoscopic images. Its high NPV value could make it suited for high-risk patient screening. A large clinical study to confirm these results is needed and will be pursued.

scholarly journals Automated Classification of Skin Lesions: From Pixels to Practice

2018 ◽  
Vol 138 (10) ◽  
pp. 2108-2110 ◽  
Author(s):  
Akhila Narla ◽  
Brett Kuprel ◽  
Kavita Sarin ◽  
Roberto Novoa ◽  
Justin Ko
Keyword(s):  
Skin Lesions ◽  
Automated Classification

Segmented and Non-Segmented Skin Lesions Classification Using Transfer Learning and Adaptive Moment Learning Rate Technique Using Pretrained Convolutional Neural Network

2019 ◽  
Vol 42 ◽  
pp. 67-78 ◽  
Author(s):  
Ali Mohammad Alqudah ◽  
Hiam Alquraan ◽  
Isam Abu Qasmieh
Keyword(s):  
Skin Lesion ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Skin Lesions ◽  
Learning Rate ◽  
Seborrheic Keratosis ◽  
Severe Problem ◽  
Deep Learning Algorithm ◽  
Reliable Classification ◽  
High Degree

A skin lesion is a very severe problem, especially in coastal countries. Early detection by a highly reliable classification of skin lesion causes a great reduction in the mortality rate. Recognition of melanoma is a complicated issue due to the high degree of visual similarities between melanoma and non-melanoma lesions. Various studies are carried out to overcome this problem and to obtain accurate screening of skin lesion, where the most recent method for segmenting and classifying the lesion is based on a deep learning algorithm. In this paper, (GoogleNet) and (AlexNet) are employed with transfer learning and optimization gradient descent adaptive momentum learning rate (ADAM). The proposed method is applied on Archive International Skin Imaging Collaboration (ISIC) database to classify images into three main classes (benign, melanoma, seborrheic keratosis) under the two scenarios; segmented and non-segmented lesion images. The overall accuracy of the non-segmented classification database is 92.2% and 89.8% for the non-segmented dataset. Utilizing optimization algorithm (ADAM) leads to a significant improvement in the classification results when they are compared with previous studies.

scholarly journals Design publicity of black box algorithms: a support to the epistemic and ethical justifications of medical AI systems

2021 ◽  
pp. medethics-2021-107482
Author(s):  
Andrea Ferrario
Keyword(s):  
Medical Practice ◽  
Skin Lesions ◽  
Black Box ◽  
Automated Classification ◽  
Ethical Challenges ◽  
Reliability Indicators ◽  
Peculiar Form ◽  
Design Requirements ◽  
Ethical Basis

In their article ‘Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI’, Durán and Jongsma discuss the epistemic and ethical challenges raised by black box algorithms in medical practice. The opacity of black box algorithms is an obstacle to the trustworthiness of their outcomes. Moreover, the use of opaque algorithms is not normatively justified in medical practice. The authors introduce a formalism, called computational reliabilism, which allows generating justified beliefs on the algorithm reliability and trustworthy outcomes of artificial intelligence (AI) systems by means of epistemic warrants, called reliability indicators. However, they remark the need for reliability indicators specific to black box algorithms and that justified knowledge is not sufficient to justify normatively the actions of the physicians using medical AI systems. Therefore, Durán and Jongsma advocate for a more transparent design and implementation of black box algorithms, providing a series of recommendations to mitigate the epistemic and ethical challenges behind their use in medical practice. In this response, I argue that a peculiar form of black box algorithm transparency, called design publicity, may efficiently implement these recommendations. Design publicity encodes epistemic, that is, reliability indicators, and ethical recommendations for black box algorithms by means of four subtypes of transparency. These target the values and goals, their translation into design requirements, the performance and consistency of the algorithm altogether. I discuss design publicity applying it to a use case focused on the automated classification of skin lesions from medical images.

Sign in / Sign up

Export Citation Format

Share Document