Kernel sparse representation based model for skin lesions segmentation and classification

Precision in Dermatology: Developing an Optimal Feature Selection Framework for Skin Lesion Classification

Melanoma is widely recognized as one of the most lethal forms of skin cancer, with its incidence showing an upward trend in recent years. Nonetheless, the timely detection of this malignancy substantially enhances the likelihood of patients' long-term survival. Several computer-based methods have recently been proposed, in the pursuit of diagnosing skin lesions at their early stages. Despite achieving some level of success, there still remains a margin of error that the machine learning community considers to be an unresolved research challenge. The primary objective of this study was to maximize the input feature information by combining multiple deep models in the first phase, and then to avoid noisy and redundant information by downsampling the feature set, using a novel evolutionary feature selection technique, in the second phase. By maintaining the integrity of the original feature space, the proposed idea generated highly discriminant feature information. Recent deep models, including Darknet53, DenseNet201, InceptionV3, and InceptionResNetV2, were employed in our study, for the purpose of feature extraction. Additionally, transfer learning was leveraged, to enhance the performance of our approach. In the subsequent phase, the extracted feature information from the chosen pre-existing models was combined, with the aim of preserving maximum information, prior to undergoing the process of feature selection, using a novel entropy-controlled gray wolf optimization (ECGWO) algorithm. The integration of fusion and selection techniques was employed, initially to incorporate the feature vector with a high level of information and, subsequently, to eliminate redundant and irrelevant feature information. The effectiveness of our concept is supported by an assessment conducted on three benchmark dermoscopic datasets: PH2, ISIC-MSK, and ISIC-UDA. In order to validate the proposed methodology, a comprehensive evaluation was conducted, including a rigorous comparison to established techniques in the field.

An Efficient Artificial Rabbits Optimization Based on Mutation Strategy For Skin Cancer Prediction

Accurate skin lesion diagnosis is critical for the early detection of melanoma. However, the existing approaches are unable to attain substantial levels of accuracy. Recently, pre-trained Deep Learning (DL) models have been applied to tackle and improve efficiency on tasks such as skin cancer detection instead of training models from scratch. Therefore, we develop a robust model for skin cancer detection with a DL-based model as a feature extraction backbone, which is achieved using MobileNetV3 architecture. In addition, a novel algorithm called the Improved Artificial Rabbits Optimizer (IARO) is introduced, which uses the Gaussian mutation and crossover operator to ignore the unimportant features from those features extracted using MobileNetV3. The PH2, ISIC-2016, and HAM10000 datasets are used to validate the developed approach's efficiency. The empirical results show that the developed approach yields outstanding accuracy results of 87.17% on the ISIC-2016 dataset, 96.79% on the PH2 dataset, and 88.71 % on the HAM10000 dataset. Experiments show that the IARO can significantly improve the prediction of skin cancer.

Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search

Recently, pre-trained deep learning (DL) models have been employed to tackle and enhance the performance on many tasks such as skin cancer detection instead of training models from scratch. However, the existing systems are unable to attain substantial levels of accuracy. Therefore, we propose, in this paper, a robust skin cancer detection framework for to improve the accuracy by extracting and learning relevant image representations using a MobileNetV3 architecture. Thereafter, the extracted features are used as input to a modified Hunger Games Search (HGS) based on Particle Swarm Optimization (PSO) and Dynamic-Opposite Learning (DOLHGS). This modification is used as a novel feature selection to alloacte the most relevant feature to maximize the model's performance. For evaluation of the efficiency of the developed DOLHGS, the ISIC-2016 dataset and the PH2 dataset were employed, including two and three categories, respectively. The proposed model has accuracy 88.19% on the ISIC-2016 dataset and 96.43% on PH2. Based on the experimental results, the proposed approach showed more accurate and efficient performance in skin cancer detection than other well-known and popular algorithms in terms of classification accuracy and optimized features.

Multiclass skin lesion localization and classification using deep learning based features fusion and selection framework for smart healthcare

BACKGROUND

The idea of smart healthcare has gradually gained attention as a result of the information technology industry's rapid development. Smart healthcare uses next-generation technologies i.e., artificial intelligence (AI) and Internet of Things (IoT), to intelligently transform current medical methods to make them more efficient, dependable and individualized. One of the most prominent uses of telemedicine and e-health in medical image analysis is teledermatology. Telecommunications technologies are used in this industry to send medical information to professionals. Teledermatology is a useful method for the identification of skin lesions, particularly in rural locations, because the skin is visually perceptible. One of the most recent tools for diagnosing skin cancer is dermoscopy. To classify skin malignancies, numerous computational approaches have been proposed in the literature. However, difficulties still exist i.e., lesions with low contrast, imbalanced datasets, high level of memory complexity, and the extraction of redundant features.

METHODS

In this work, a unified CAD model is proposed based on a deep learning framework for skin lesion segmentation and classification. In the proposed approach, the source dermoscopic images are initially pre-processed using a contrast enhancement based modified bio-inspired multiple exposure fusion approach. In the second stage, a custom 26-layered convolutional neural network (CNN) architecture is designed to segment the skin lesion regions. In the third stage, four pre-trained CNN models (Xception, ResNet-50, ResNet-101 and VGG16) are modified and trained using transfer learning on the segmented lesion images. In the fourth stage, the deep features vectors are extracted from all the CNN models and fused using the convolutional sparse image decomposition fusion approach. In the fifth stage, the univariate measurement and Poisson distribution feature selection approach is used for the best features selection for classification. Finally, the selected features are fed to the multi-class support vector machine (MC-SVM) for the final classification.

RESULTS

The proposed approach employed to the HAM10000, ISIC2018, ISIC2019, and PH2 datasets and achieved an accuracy of 98.57%, 98.62%, 93.47%, and 98.98% respectively which are better than previous works.

CONCLUSION

When compared to renowned state-of-the-art methods, experimental results show that the proposed skin lesion detection and classification approach achieved higher performance in terms of both visually and enhanced quantitative evaluation with enhanced accuracy.

Skin lesion classification of dermoscopic images using machine learning and convolutional neural network

Detecting dangerous illnesses connected to the skin organ, particularly malignancy, requires the identification of pigmented skin lesions. Image detection techniques and computer classification capabilities can boost skin cancer detection accuracy. The dataset used for this research work is based on the HAM10000 dataset which consists of 10015 images. The proposed work has chosen a subset of the dataset and performed augmentation. A model with data augmentation tends to learn more distinguishing characteristics and features rather than a model without data augmentation. Involving data augmentation can improve the accuracy of the model. But that model cannot give significant results with the testing data until it is robust. The k-fold cross-validation technique makes the model robust which has been implemented in the proposed work. We have analyzed the classification accuracy of the Machine Learning algorithms and Convolutional Neural Network models. We have concluded that Convolutional Neural Network provides better accuracy compared to other machine learning algorithms implemented in the proposed work. In the proposed system, as the highest, we obtained an accuracy of 95.18% with the CNN model. The proposed work helps early identification of seven classes of skin disease and can be validated and treated appropriately by medical practitioners.

AHA-AO: Artificial Hummingbird Algorithm with Aquila Optimization for Efficient Feature Selection in Medical Image Classification

This paper presents a system for medical image diagnosis that uses transfer learning (TL) and feature selection techniques. The main aim of TL on pre-trained models such as MobileNetV3 is to extract features from raw images. Here, a novel feature selection optimization algorithm called the Artificial Hummingbird Algorithm based on Aquila Optimization (AHA-AO) is proposed. The AHA-AO is used to select only the most relevant features and ensure the improvement of the overall model classification. Our methodology was evaluated using four datasets, namely, ISIC-2016, PH2, Chest-XRay, and Blood-Cell. We compared the proposed feature selection algorithm with five of the most popular feature selection optimization algorithms. We obtained an accuracy of 87.30% for the ISIC-2016 dataset, 97.50% for the PH2 dataset, 86.90% for the Chest-XRay dataset, and 88.60% for the Blood-cell dataset. The AHA-AO outperformed the other optimization techniques. Moreover, the developed AHA-AO was faster than the other feature selection models during the process of determining the relevant features. The proposed feature selection algorithm successfully improved the performance and the speed of the overall deep learning models.

Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things

The Internet of Medical Things (IoMT) has dramatically benefited medical professionals that patients and physicians can access from all regions. Although the automatic detection and prediction of diseases such as melanoma and leukemia is still being investigated and studied in IoMT, existing approaches are not able to achieve a high degree of efficiency. Thus, with a new approach that provides better results, patients would access the adequate treatments earlier and the death rate would be reduced. Therefore, this paper introduces an IoMT proposal for medical images' classification that may be used anywhere, i.e., it is an ubiquitous approach. It was designed in two stages: first, we employ a transfer learning (TL)-based method for feature extraction, which is carried out using MobileNetV3; second, we use the chaos game optimization (CGO) for feature selection, with the aim of excluding unnecessary features and improving the performance, which is key in IoMT. Our methodology was evaluated using ISIC-2016, PH2, and Blood-Cell datasets. The experimental results indicated that the proposed approach obtained an accuracy of 88.39% on ISIC-2016, 97.52% on PH2, and 88.79% on Blood-cell datsets. Moreover, our approach had successful performances for the metrics employed compared to other existing methods.

A Decision Support System for Melanoma Diagnosis from Dermoscopic Images

Innovative technologies in dermatology allow for the early screening of skin cancer, which results in a reduction in the mortality rate and surgical treatments. The diagnosis of melanoma is complex not only because of the number of different lesions but because of the high similarity amongst skin lesions of different nature; hence, human vision and physician experience still play a major role. The adoption of automatic systems would aid clinical assessment and make the diagnosis reproducible by eliminating inter- and intra-observer variabilities. In our paper, we describe a computer-aided system for the early diagnosis of melanoma in dermoscopic images. A soft pre-processing phase is performed so as to avoid the loss of details both in texture, colors, and contours, and color-based image segmentation is later carried out using k-means. Features linked to both geometric properties and color characteristics are used to analyze skin lesions through a support vector machine classifier. The PH2 public database is used for the assessment of the procedure’s sensitivity, specificity, and accuracy. A statistical approach is carried out to establish the impact of image quality on performance. The obtained results show remarkable achievements, so our computer-aided approach should be suitable as a Decision Support System for melanoma detection.

Skin Lesion Extraction Using Multiscale Morphological Local Variance Reconstruction Based Watershed Transform and Fast Fuzzy C-Means Clustering

Early identification of melanocytic skin lesions increases the survival rate for skin cancer patients. Automated melanocytic skin lesion extraction from dermoscopic images using the computer vision approach is a challenging task as the lesions present in the image can be of different colors, there may be a variation of contrast near the lesion boundaries, lesions may have different sizes and shapes, etc. Therefore, lesion extraction from dermoscopic images is a fundamental step for automated melanoma identification. In this article, a watershed transform based on the fast fuzzy c-means (FCM) clustering algorithm is proposed for the extraction of melanocytic skin lesion from dermoscopic images. Initially, the proposed method removes the artifacts from the dermoscopic images and enhances the texture regions. Further, it is filtered using a Gaussian filter and a local variance filter to enhance the lesion boundary regions. Later, the watershed transform based on MMLVR (multiscale morphological local variance reconstruction) is introduced to acquire the superpixels of the image with accurate boundary regions. Finally, the fast FCM clustering technique is implemented in the superpixels of the image to attain the final lesion extraction result. The proposed method is tested in the three publicly available skin lesion image datasets, i.e., ISIC 2016, ISIC 2017 and ISIC 2018. Experimental evaluation shows that the proposed method achieves a good result.

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.

Prediction of skin disease using a new cytological taxonomy based on cytology and pathology with deep residual learning method

With the development of artificial intelligence, technique improvement of the classification of skin disease is addressed. However, few study concerned on the current classification system of International Classification of Diseases, Tenth Revision (ICD)-10 on Diseases of the skin and subcutaneous tissue, which is now globally used for classification of skin disease. This study was aimed to develop a new taxonomy of skin disease based on cytology and pathology, and test its predictive effect on skin disease compared to ICD-10. A new taxonomy (Taxonomy 2) containing 6 levels (Project 2–4) was developed based on skin cytology and pathology, and represents individual diseases arranged in a tree structure with three root nodes representing: (1) Keratinogenic diseases, (2) Melanogenic diseases, and (3) Diseases related to non-keratinocytes and non-melanocytes. The predictive effects of the new taxonomy including accuracy, precision, recall, F1, and Kappa were compared with those of ICD-10 on Diseases of the skin and subcutaneous tissue (Taxonomy 1, Project 1) by Deep Residual Learning method. For each project, 2/3 of the images were included as training group, and the rest 1/3 of the images acted as test group according to the category (class) as the stratification variable. Both train and test groups in the Projects (2 and 3) from Taxonomy 2 had higher F1 and Kappa scores without statistical significance on the prediction of skin disease than the corresponding groups in the Project 1 from Taxonomy 1, however both train and test groups in Project 4 had a statistically significantly higher F1-score than the corresponding groups in Project 1 (P = 0.025 and 0.005, respectively). The results showed that the new taxonomy developed based on cytology and pathology has an overall better performance on predictive effect of skin disease than the ICD-10 on Diseases of the skin and subcutaneous tissue. The level 5 (Project 4) of Taxonomy 2 is better on extension to unknown data of diagnosis system assisted by AI compared to current used classification system from ICD-10, and may have the potential application value in clinic of dermatology.

A hierarchical three-step superpixels and deep learning framework for skin lesion classification

Skin cancer is one of the most common and dangerous cancer that exists worldwide. Malignant melanoma is one of the most dangerous skin cancer types has a high mortality rate. An estimated 196,060 melanoma cases will be diagnosed in 2020 in the USA. Many computerized techniques are presented in the past to diagnose skin lesions, but they are still failing to achieve significant accuracy. To improve the existing accuracy, we proposed a hierarchical framework based on two-dimensional superpixels and deep learning. First, we enhance the contrast of original dermoscopy images by fusing local and global enhanced images. The entire enhanced images are utilized in the next step to segmentation skin lesions using three-step superpixel lesion segmentation. The segmented lesions are mapped over the whole enhanced dermoscopy images and obtained only segmented color images. Then, a deep learning model (ResNet-50) is applied to these mapped images and learned features through transfer learning. The extracted features are further optimized using an improved grasshopper optimization algorithm, which is later classified through the Naïve Bayes classifier. The proposed hierarchical method has been evaluated on three datasets (Ph2, ISBI2016, and HAM1000), consisting of three, two, and seven skin cancer classes. On these datasets, our method achieved an accuracy of 95.40%, 91.1%, and 85.50%, respectively. The results show that this method can be helpful for the classification of skin cancer with improved accuracy.

NSCR-Based DenseNet for Lung Tumor Recognition Using Chest CT Image

Nonnegative sparse representation has become a popular methodology in medical analysis and diagnosis in recent years. In order to resolve network degradation, higher dimensionality in feature extraction, data redundancy, and other issues faced when medical images parameters are trained using convolutional neural networks. Lung tumors in chest CT image based on nonnegative, sparse, and collaborative representation classification of DenseNet (DenseNet-NSCR) are proposed by this paper: firstly, initialization parameters of pretrained DenseNet model using transfer learning; secondly, training DenseNet using CT images to extract feature vectors for the full connectivity layer; thirdly, a nonnegative, sparse, and collaborative representation (NSCR) is used to represent the feature vector and solve the coding coefficient matrix; fourthly, the residual similarity is used for classification. The experimental results show that the DenseNet-NSCR classification is better than the other models, and the various evaluation indexes such as specificity and sensitivity are also high, and the method has better robustness and generalization ability through comparison experiment using AlexNet, GoogleNet, and DenseNet-201 models.

Fast fully automatic skin lesions segmentation probabilistic with Parzen window

Cutaneous melanoma accounts for over 90% of all melanoma, causing up to 55,500 annual deaths. However, it is a potentially curable type of cancer. Since melanoma is potentially curable, the disease's mortality rate is directly linked to late detection. This work proposes an approach that presents the balance between time and efficiency. This paper proposes the method of fast and automatic segmentation of skin lesions using probabilistic characteristics with the Parzen window (SPPW). The results obtained by the method were based on PH2 and ISIC datasets. The SPPW approach reached the following averages between the two datasets Specificity of 98.55%, Accuracy of 95.48%, Dice of 91.12%, Sensitivity of 88.45%, Mattheus of 87.86%, and Jaccard Index of 84.90%. The highlights of the proposed method are its short average segmentation time per image, and its metrics values, which are often higher than the ones obtained by other methods. Therefore, the SPPW method of segmentation is a quick, viable, and easily accessible option to aid in the diagnosis of diseased skin.

Non-Local SVD Denoising of MRI Based on Sparse Representations

Magnetic Resonance (MR) Imaging is a diagnostic technique that produces noisy images, which must be filtered before processing to prevent diagnostic errors. However, filtering the noise while keeping fine details is a difficult task. This paper presents a method, based on sparse representations and singular value decomposition (SVD), for non-locally denoising MR images. The proposed method prevents blurring, artifacts, and residual noise. Our method is composed of three stages. The first stage divides the image into sub-volumes, to obtain its sparse representation, by using the KSVD algorithm. Then, the global influence of the dictionary atoms is computed to upgrade the dictionary and obtain a better reconstruction of the sub-volumes. In the second stage, based on the sparse representation, the noise-free sub-volume is estimated using a non-local approach and SVD. The noise-free voxel is reconstructed by aggregating the overlapped voxels according to the rarity of the sub-volumes it belongs, which is computed from the global influence of the atoms. The third stage repeats the process using a different sub-volume size for producing a new filtered image, which is averaged with the previously filtered images. The results provided show that our method outperforms several state-of-the-art methods in both simulated and real data.