Skin Cancer Diagnostics with an All-Inclusive Smartphone Application

Upender Kalwa; Christopher Legner; Taejoon Kong; Santosh Pandey

doi:10.3390/sym11060790

Skin Cancer Diagnostics with an All-Inclusive Smartphone Application

Symmetry ◽

10.3390/sym11060790 ◽

2019 ◽

Vol 11 (6) ◽

pp. 790 ◽

Cited By ~ 8

Author(s):

Upender Kalwa ◽

Christopher Legner ◽

Taejoon Kong ◽

Santosh Pandey

Keyword(s):

Skin Cancer ◽

Performance Metrics ◽

Sampling Technique ◽

Smartphone Application ◽

Skin Lesions ◽

Cancer Diagnostics ◽

Support Vector ◽

Diagnostic Systems ◽

Image Capture ◽

Public Datasets

Among the different types of skin cancer, melanoma is considered to be the deadliest and is difficult to treat at advanced stages. Detection of melanoma at earlier stages can lead to reduced mortality rates. Desktop-based computer-aided systems have been developed to assist dermatologists with early diagnosis. However, there is significant interest in developing portable, at-home melanoma diagnostic systems which can assess the risk of cancerous skin lesions. Here, we present a smartphone application that combines image capture capabilities with preprocessing and segmentation to extract the Asymmetry, Border irregularity, Color variegation, and Diameter (ABCD) features of a skin lesion. Using the feature sets, classification of malignancy is achieved through support vector machine classifiers. By using adaptive algorithms in the individual data-processing stages, our approach is made computationally light, user friendly, and reliable in discriminating melanoma cases from benign ones. Images of skin lesions are either captured with the smartphone camera or imported from public datasets. The entire process from image capture to classification runs on an Android smartphone equipped with a detachable 10x lens, and processes an image in less than a second. The overall performance metrics are evaluated on a public database of 200 images with Synthetic Minority Over-sampling Technique (SMOTE) (80% sensitivity, 90% specificity, 88% accuracy, and 0.85 area under curve (AUC)) and without SMOTE (55% sensitivity, 95% specificity, 90% accuracy, and 0.75 AUC). The evaluated performance metrics and computation times are comparable or better than previous methods. This all-inclusive smartphone application is designed to be easy-to-download and easy-to-navigate for the end user, which is imperative for the eventual democratization of such medical diagnostic systems.

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Proposed Threshold Algorithm for Accurate Segmentation for Skin Lesion

International Journal of Biomedical and Clinical Engineering ◽

10.4018/ijbce.2015070104 ◽

2015 ◽

Vol 4 (2) ◽

pp. 40-47

Author(s):

T. Y. Satheesha ◽

D. Sathyanarayana ◽

M. N. Giri Prasad

Keyword(s):

Skin Cancer ◽

Skin Lesion ◽

Skin Lesions ◽

Large Set ◽

Threshold Values ◽

Diagnostic Systems ◽

Automated Diagnosis ◽

Algorithm Efficiency ◽

Threshold Algorithms ◽

Threshold Technique

Automated diagnosis of skin cancer can be easily achieved only by effective segmentation of skin lesion. But this is a highly challenging task due to the presence of intensity variations in the images of skin lesions. The authors here, have presented a histogram analysis based fuzzy C mean threshold technique to overcome the drawbacks. This not only reduces the computational complexity but also unifies advantages of soft and hard threshold algorithms. Calculation of threshold values even the presence of abrupt intensity variations is simplified. Segmentation of skin lesions is easily achieved, in a more efficient way in the following algorithm. The experimental verification here is done on a large set of skin lesion images containing every possible artifacts which highly contributes to reversed segmentation outputs. This algorithm efficiency was measured based on a comparison with other prominent threshold methods. This approach has performed reasonably well and can be implemented in the expert skin cancer diagnostic systems

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A new classification strategy for human activity recognition using cost sensitive support vector machines for imbalanced data

Kybernetes ◽

10.1108/k-07-2014-0138 ◽

2014 ◽

Vol 43 (8) ◽

pp. 1150-1164 ◽

Cited By ~ 9

Author(s):

Bilal M’hamed Abidine ◽

Belkacem Fergani ◽

Mourad Oussalah ◽

Lamya Fergani

Keyword(s):

Support Vector Machines ◽

Probabilistic Models ◽

Conditional Random Fields ◽

Performance Metrics ◽

Imbalanced Data ◽

Sampling Technique ◽

Support Vector ◽

Data Set ◽

Content Type ◽

Vector Machines

Purpose – The task of identifying activity classes from sensor information in smart home is very challenging because of the imbalanced nature of such data set where some activities occur more frequently than others. Typically probabilistic models such as Hidden Markov Model (HMM) and Conditional Random Fields (CRF) are known as commonly employed for such purpose. The paper aims to discuss these issues. Design/methodology/approach – In this work, the authors propose a robust strategy combining the Synthetic Minority Over-sampling Technique (SMOTE) with Cost Sensitive Support Vector Machines (CS-SVM) with an adaptive tuning of cost parameter in order to handle imbalanced data problem. Findings – The results have demonstrated the usefulness of the approach through comparison with state of art of approaches including HMM, CRF, the traditional C-Support vector machines (C-SVM) and the Cost-Sensitive-SVM (CS-SVM) for classifying the activities using binary and ubiquitous sensors. Originality/value – Performance metrics in the experiment/simulation include Accuracy, Precision/Recall and F measure.

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Skin Cancer Detection Using Support Vector Machine Learning Classification based on Particle Swarm Optimization Capabilities

Transactions on Machine Learning and Artificial Intelligence ◽

10.14738/tmlai.84.8415 ◽

2020 ◽

Vol 8 (4) ◽

pp. 01-13

Author(s):

Ding-Yu Fei ◽

Osamah Almasiri ◽

Azhar Rafig

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Skin Cancer ◽

Algorithm Design ◽

Particle Swarm ◽

Clinical Decision ◽

Skin Lesions ◽

Support Vector ◽

Swarm Optimization ◽

Machine Learning Classification

Skin cancer continues to be a common malignancy that has steadily increased each year. The need for early detection of such skin lesions is critical to preventing further medical complications. The main method for detection of skin cancer is by microscopic examination of skin lesions. Great efforts have been placed to use computer aided technologies for the analysis of skin lesions. In this study, we present a method for an algorithm design using Support Vector Machine (SVM) learning classification based on Particle swarm optimization (PSO) principles in order to improve the accuracy of skin lesion image analysis and classification for further diagnosis. Hospital Pedro Hispano (PH²) dataset with 200 images is used for this study. The method presented here incorporates 46 texture features in order to complete comprehensive image analytics and classification. The proposed method demonstrates an opportunity to explore best possible criteria in image analytics for clinical decision support.

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Assessing Generalizability of Deep Learning Models Trained on Standardized and Nonstandardized Images and Their Performance Against Teledermatologists (Preprint)

10.2196/preprints.35391 ◽

2021 ◽

Author(s):

Ibukun Oloruntoba ◽

Toan D Nguyen ◽

Zongyuan Ge ◽

Tine Vestergaard ◽

Victoria Mar

Keyword(s):

Skin Cancer ◽

Area Under The Curve ◽

Image Data ◽

Skin Lesions ◽

Training Image ◽

Data Sets ◽

Image Capture ◽

Data Set ◽

Unseen Data ◽

Test Variability

BACKGROUND Convolutional neural networks (CNNs) are a type of artificial intelligence that show promise as a diagnostic aid for skin cancer. However, the majority are trained using retrospective image data sets of varying quality and image capture standardization. OBJECTIVE The aim of our study is to use CNN models with the same architecture, but different training image sets, and test variability in performance when classifying skin cancer images in different populations, acquired with different devices. Additionally, we wanted to assess the performance of the models against Danish teledermatologists when tested on images acquired from Denmark. METHODS Three CNNs with the same architecture were trained. CNN-NS was trained on 25,331 nonstandardized images taken from the International Skin Imaging Collaboration using different image capture devices. CNN-S was trained on 235,268 standardized images, and CNN-S2 was trained on 25,331 standardized images (matched for number and classes of training images to CNN-NS). Both standardized data sets (CNN-S and CNN-S2) were provided by Molemap using the same image capture device. A total of 495 Danish patients with 569 images of skin lesions predominantly involving Fitzpatrick skin types II and III were used to test the performance of the models. Four teledermatologists independently diagnosed and assessed the images taken of the lesions. Primary outcome measures were sensitivity, specificity, and area under the curve of the receiver operating characteristic (AUROC). RESULTS A total of 569 images were taken from 495 patients (n=280, 57% women, n=215, 43% men; mean age 55, SD 17 years) for this study. On these images, CNN-S achieved an AUROC of 0.861 (95% CI 0.830-0.889; P<.001), and CNN-S2 achieved an AUROC of 0.831 (95% CI 0.798-0.861; P=.009), with both outperforming CNN-NS, which achieved an AUROC of 0.759 (95% CI 0.722-0.794; P<.001; P=.009). When the CNNs were matched to the mean sensitivity and specificity of the teledermatologists, the model’s resultant sensitivities and specificities were surpassed by the teledermatologists. However, when compared to CNN-S, the differences were not statistically significant (P=.10; P=.05). Performance across all CNN models and teledermatologists was influenced by the image quality. CONCLUSIONS CNNs trained on standardized images had improved performance and therefore greater generalizability in skin cancer classification when applied to an unseen data set. This is an important consideration for future algorithm development, regulation, and approval. Further, when tested on these unseen test images, the teledermatologists clinically outperformed all the CNN models; however, the difference was deemed to be statistically insignificant when compared to CNN-S.

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Categorization of Common Pigmented Skin Lesions (CPSL) using Multi-Deep Features and Support Vector Machine

10.21203/rs.3.rs-136988/v1 ◽

2021 ◽

Author(s):

SANTI BEHERA ◽

PRABIRA SETHY

Keyword(s):

Support Vector Machine ◽

Skin Cancer ◽

Principal Component ◽

Skin Lesions ◽

Classification Model ◽

Healthcare Sector ◽

Support Vector ◽

Svm Classifier ◽

Deep Feature ◽

Pigmented Skin Lesions

Abstract The skin is the main organ. It is approximately 8 pounds for the average adult. Our skin is a truly wonderful organ. It isolates us and shields our bodies from hazards. However, the skin is also vulnerable to damage and distracted from its original appearance; brown, black, or blue, or combinations of those colors, known as pigmented skin lesions. These common pigmented skin lesions (CPSL) are the leading factor of skin cancer, or can say these are the primary causes of skin cancer. In the healthcare sector, the categorization of CPSL is the main problem because of inaccurate outputs, overfitting, and higher computational costs. Hence, we proposed a classification model based on multi-deep feature and support vector machine (SVM) for the classification of CPSL. The proposed system comprises two phases: first, evaluate the 11 CNN model's performance in the deep feature extraction approach with SVM. Then, concatenate the top performed three CNN model's deep features and with the help of SVM to categorize the CPSL. In the second step, 8192 and 12288 features are obtained by combining binary and triple networks of 4096 features from the top performed CNN model. These features are also given to the SVM classifiers. The SVM results are also evaluated with principal component analysis (PCA) algorithm to the combined feature of 8192 and 12288. The highest results are obtained with 12288 features. The experimentation results, the combination of the deep feature of Alexnet, VGG16 & VGG19, achieved the highest accuracy of 91.7% using SVM classifier. As a result, the results show that the proposed methods are a useful tool for CPSL classification.

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Assessing Generalizability of Deep Learning Models Trained on Standardized and Nonstandardized Images and Their Performance Against Teledermatologists

Iproceedings ◽

10.2196/35391 ◽

2021 ◽

Vol 6 (1) ◽

pp. e35391

Author(s):

Ibukun Oloruntoba ◽

Toan D Nguyen ◽

Zongyuan Ge ◽

Tine Vestergaard ◽

Victoria Mar

Keyword(s):

Skin Cancer ◽

Conflicts Of Interest ◽

Area Under The Curve ◽

Image Data ◽

Skin Lesions ◽

Training Image ◽

Data Sets ◽

Image Capture ◽

Data Set ◽

Unseen Data

Background Convolutional neural networks (CNNs) are a type of artificial intelligence that show promise as a diagnostic aid for skin cancer. However, the majority are trained using retrospective image data sets of varying quality and image capture standardization. Objective The aim of our study is to use CNN models with the same architecture, but different training image sets, and test variability in performance when classifying skin cancer images in different populations, acquired with different devices. Additionally, we wanted to assess the performance of the models against Danish teledermatologists when tested on images acquired from Denmark. Methods Three CNNs with the same architecture were trained. CNN-NS was trained on 25,331 nonstandardized images taken from the International Skin Imaging Collaboration using different image capture devices. CNN-S was trained on 235,268 standardized images, and CNN-S2 was trained on 25,331 standardized images (matched for number and classes of training images to CNN-NS). Both standardized data sets (CNN-S and CNN-S2) were provided by Molemap using the same image capture device. A total of 495 Danish patients with 569 images of skin lesions predominantly involving Fitzpatrick skin types II and III were used to test the performance of the models. Four teledermatologists independently diagnosed and assessed the images taken of the lesions. Primary outcome measures were sensitivity, specificity, and area under the curve of the receiver operating characteristic (AUROC). Results A total of 569 images were taken from 495 patients (n=280, 57% women, n=215, 43% men; mean age 55, SD 17 years) for this study. On these images, CNN-S achieved an AUROC of 0.861 (95% CI 0.830-0.889; P<.001), and CNN-S2 achieved an AUROC of 0.831 (95% CI 0.798-0.861; P=.009), with both outperforming CNN-NS, which achieved an AUROC of 0.759 (95% CI 0.722-0.794; P<.001; P=.009). When the CNNs were matched to the mean sensitivity and specificity of the teledermatologists, the model’s resultant sensitivities and specificities were surpassed by the teledermatologists. However, when compared to CNN-S, the differences were not statistically significant (P=.10; P=.05). Performance across all CNN models and teledermatologists was influenced by the image quality. Conclusions CNNs trained on standardized images had improved performance and therefore greater generalizability in skin cancer classification when applied to an unseen data set. This is an important consideration for future algorithm development, regulation, and approval. Further, when tested on these unseen test images, the teledermatologists clinically outperformed all the CNN models; however, the difference was deemed to be statistically insignificant when compared to CNN-S. Conflicts of Interest VM received speakers fees from Merck, Eli Lily, Novartis and Bristol Myers Squibb. VM is the principal investigator for a clinical trial funded by the Victorian Department of Health and Human Services with 1:1 contribution from MoleMap.

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Proposed Threshold Algorithm for Accurate Segmentation for Skin Lesion

Oncology ◽

10.4018/978-1-5225-0549-5.ch009 ◽

2017 ◽

pp. 302-309

Author(s):

T. Y. Satheesha ◽

D. Sathyanarayana ◽

M. N. Giri Prasad

Keyword(s):

Computational Complexity ◽

Skin Cancer ◽

Skin Lesion ◽

Skin Lesions ◽

Large Set ◽

Diagnostic Systems ◽

Automated Diagnosis ◽

Algorithm Efficiency ◽

Threshold Algorithms ◽

Threshold Technique

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

BioMed Research International ◽

10.1155/2016/4381972 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8 ◽

Cited By ~ 21

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.

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Developing a Recognition System for Diagnosing Melanoma Skin Lesions Using Artificial Intelligence Algorithms

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/9998379 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Fawaz Waselallah Alsaade ◽

Theyazn H. H. Aldhyani ◽

Mosleh Hmoud Al-Adhaileh

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Skin Cancer ◽

Skin Diseases ◽

Skin Lesions ◽

Machine Learning Algorithms ◽

Contour Method ◽

Ann Model ◽

Diagnostic Systems ◽

Feature Based

In recent years, computerized biomedical imaging and analysis have become extremely promising, more interesting, and highly beneficial. They provide remarkable information in the diagnoses of skin lesions. There have been developments in modern diagnostic systems that can help detect melanoma in its early stages to save the lives of many people. There is also a significant growth in the design of computer-aided diagnosis (CAD) systems using advanced artificial intelligence. The purpose of the present research is to develop a system to diagnose skin cancer, one that will lead to a high level of detection of the skin cancer. The proposed system was developed using deep learning and traditional artificial intelligence machine learning algorithms. The dermoscopy images were collected from the PH2 and ISIC 2018 in order to examine the diagnose system. The developed system is divided into feature-based and deep leaning. The feature-based system was developed based on feature-extracting methods. In order to segment the lesion from dermoscopy images, the active contour method was proposed. These skin lesions were processed using hybrid feature extractions, namely, the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) methods to extract the texture features. The obtained features were then processed using the artificial neural network (ANNs) algorithm. In the second system, the convolutional neural network (CNNs) algorithm was applied for the efficient classification of skin diseases; the CNNs were pretrained using large AlexNet and ResNet50 transfer learning models. The experimental results show that the proposed method outperformed the state-of-art methods for HP2 and ISIC 2018 datasets. Standard evaluation metrics like accuracy, specificity, sensitivity, precision, recall, and F -score were employed to evaluate the results of the two proposed systems. The ANN model achieved the highest accuracy for PH2 (97.50%) and ISIC 2018 (98.35%) compared with the CNN model. The evaluation and comparison, proposed systems for classification and detection of melanoma are presented.

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A Machine Learning Method Based on the Combination of Nonlinear and Texture Features to Diagnose Malignant Melanoma From Dermoscopic Images

10.21203/rs.3.rs-257616/v1 ◽

2021 ◽

Author(s):

Sepehr Salem Ghahfarrokhi ◽

Hamed Khodadadi

Keyword(s):

Neural Network ◽

Machine Learning ◽

Skin Cancer ◽

Active Contour Model ◽

Malignant Tumors ◽

Learning Algorithms ◽

Skin Lesions ◽

Machine Learning Algorithms ◽

Support Vector ◽

Nsga Ii

Abstract Skin cancer affects people of all skin tones, including those with darker complexions. Melanomas are known as the malignant tumors of skin cancer, resulting in an adverse prognosis, responsible for most deaths relating to skin cancer. Early diagnosis and treatment of skin cancer from dermoscopic images can significantly reduce mortality and save lives. Although several Computer-Aided Diagnosis (CAD) systems with satisfactory performance have been introduced in the literature for skin cancer detection, the high false detection rate has made it inevitable to have an expert physician for more examination. In this paper, a CAD system based on machine learning algorithms is provided to classify various skin cancer types. The proposed method uses the Online Region-based Active Contour Model (ORACM) to extract the Region Of Interest (ROI) of skin lesions. This model uses a new binary level set equation and regularization operation such as morphological opening and closing.Additionally, various combinations of different textures and nonlinear features are extracted for the ROI to show the multiple aspects of skin lesions. Several metaheuristic optimization algorithms are used to remove redundant or irrelevant features and reduce the feature space dimension. These are applied to the combination of the extracted features in which, Non-dominated Sorting Genetic Algorithm (NSGA II) as a multi-objective optimization algorithm has the best performance. Furthermore, various machine learning algorithms include K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Fitting neural network (Fit net), Feed-Forward neural network (FF net), and Pattern recognition network (Pat net) are employed for the classification. Accordingly, the best-obtained precision of 99.24% based on five-fold cross-validation is attained by the selected features of texture and nonlinear indices through NSGA II, applying the pattern net classifier. Also, the comparison between this paper's experimental results and other similar works with the same dataset demonstrates the proposed method's efficiency.

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