Skin lesion classification enhancement using border-line features – The melanoma vs nevus problem

MSRNet: Multiclass Skin Lesion Recognition Using Additional Residual Block Based Fine-Tuned Deep Models Information Fusion and Best Feature Selection

Cancer is one of the leading significant causes of illness and chronic disease worldwide. Skin cancer, particularly melanoma, is becoming a severe health problem due to its rising prevalence. The considerable death rate linked with melanoma requires early detection to receive immediate and successful treatment. Lesion detection and classification are more challenging due to many forms of artifacts such as hairs, noise, and irregularity of lesion shape, color, irrelevant features, and textures. In this work, we proposed a deep-learning architecture for classifying multiclass skin cancer and melanoma detection. The proposed architecture consists of four core steps: image preprocessing, feature extraction and fusion, feature selection, and classification. A novel contrast enhancement technique is proposed based on the image luminance information. After that, two pre-trained deep models, DarkNet-53 and DensNet-201, are modified in terms of a residual block at the end and trained through transfer learning. In the learning process, the Genetic algorithm is applied to select hyperparameters. The resultant features are fused using a two-step approach named serial-harmonic mean. This step increases the accuracy of the correct classification, but some irrelevant information is also observed. Therefore, an algorithm is developed to select the best features called marine predator optimization (MPA) controlled Reyni Entropy. The selected features are finally classified using machine learning classifiers for the final classification. Two datasets, ISIC2018 and ISIC2019, have been selected for the experimental process. On these datasets, the obtained maximum accuracy of 85.4% and 98.80%, respectively. To prove the effectiveness of the proposed methods, a detailed comparison is conducted with several recent techniques and shows the proposed framework outperforms.

Multi‐branch sustainable convolutional neural network for disease classification

Pandemic and natural disasters are growing more often, imposing even more pressure on life care services and users. There are knowledge gaps regarding how to prevent disasters and pandemics. In recent years, after heart disease, corona virus disease‐19 (COVID‐19), brain stroke, and cancer are at their peak. Different machine learning and deep learning‐based techniques are presented to detect these diseases. Existing technique uses two branches that have been used for detection and prediction of disease accurately such as brain hemorrhage. However, existing techniques have been focused on the detection of specific diseases with double‐branches convolutional neural networks (CNNs). There is a need to develop a model to detect multiple diseases at the same time using computerized tomography (CT) scan images. We proposed a model that consists of 12 branches of CNN to detect the different types of diseases with their subtypes using CT scan images and classify them more accurately. We proposed multi‐branch sustainable CNN model with deep learning architecture trained on the brain CT hemorrhage, COVID‐19 lung CT scans and chest CT scans with subtypes of lung cancers. Feature extracted automatically from preprocessed input data and passed to classifiers for classification in the form of concatenated feature vectors. Six classifiers support vector machine (SVM), decision tree (DT), K‐nearest neighbor (K‐NN), artificial neural network (ANN), naïve Bayes (NB), linear regression (LR) classifiers, and three ensembles the random forest (RF), AdaBoost, gradient boosting ensembles were tested on our model for classification and prediction. Our model achieved the best results on RF on each dataset. Respectively, on brain CT hemorrhage achieved (99.79%) accuracy, on COVID‐19 lung CT scans achieved (97.61%), and on chest CT scans dataset achieved (98.77%).

A survey, review, and future trends of skin lesion segmentation and classification

The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis.

Refined Residual Deep Convolutional Network for Skin Lesion Classification

Skin cancer is the most common type of cancer that affects humans and is usually diagnosed by initial clinical screening, which is followed by dermoscopic analysis. Automated classification of skin lesions is still a challenging task because of the high visual similarity between melanoma and benign lesions. This paper proposes a new residual deep convolutional neural network (RDCNN) for skin lesions diagnosis. The proposed neural network is trained and tested using six well-known skin cancer datasets, PH2, DermIS and Quest, MED-NODE, ISIC2016, ISIC2017, and ISIC2018. Three different experiments are carried out to measure the performance of the proposed RDCNN. In the first experiment, the proposed RDCNN is trained and tested using the original dataset images without any pre-processing or segmentation. In the second experiment, the proposed RDCNN is tested using segmented images. Finally, the utilized trained model in the second experiment is saved and reused in the third experiment as a pre-trained model. Then, it is trained again using a different dataset. The proposed RDCNN shows significant high performance and outperforms the existing deep convolutional networks.

Machine Learning and Deep Learning Algorithms for Skin Cancer Classification from Dermoscopic Images

We carry out a critical assessment of machine learning and deep learning models for the classification of skin tumors. Machine learning (ML) algorithms tested in this work include logistic regression, linear discriminant analysis, k-nearest neighbors classifier, decision tree classifier and Gaussian naive Bayes, while deep learning (DL) models employed are either based on a custom Convolutional Neural Network model, or leverage transfer learning via the use of pre-trained models (VGG16, Xception and ResNet50). We find that DL models, with accuracies up to 0.88, all outperform ML models. ML models exhibit accuracies below 0.72, which can be increased to up to 0.75 with ensemble learning. To further assess the performance of DL models, we test them on a larger and more imbalanced dataset. Metrics, such as the F-score and accuracy, indicate that, after fine-tuning, pre-trained models perform extremely well for skin tumor classification. This is most notably the case for VGG16, which exhibits an F-score of 0.88 and an accuracy of 0.88 on the smaller database, and metrics of 0.70 and 0.88, respectively, on the larger database.

Lossless Coding of Light Fields Based on 4D Minimum Rate Predictors

Common representations of light fields use four-dimensional data structures, where a given pixel is closely related not only to its spatial neighbours within the same view, but also to its angular neighbours, co-located in adjacent views. Such structure presents increased redundancy between pixels, when compared with regular single-view images. Then, these redundancies are exploited to obtain compressed representations of the light field, using prediction algorithms specifically tailored to estimate pixel values based on both spatial and angular references. This paper proposes new encoding schemes which take advantage of the four-dimensional light field data structures to improve the coding performance of Minimum Rate Predictors. The proposed methods expand previous research on lossless coding beyond the current state-of-the-art. The experimental results, obtained using both traditional datasets and others more challenging, show bit-rate savings no smaller than 10%, when compared with existing methods for lossless light field compression.

Skin lesion classification using features of 3D border lines

Machine learning algorithms are progressively assuming important roles as computational tools to support clinical diagnosis, namely in the classification of pigmented skin lesions using RGB images. Most current classification methods rely on common 2D image features derived from shape, colour or texture, which does not always guarantee the best results. This work presents a contribution to this field, by exploiting the lesions' border line characteristics using a new dimension - depth, which has not been thoroughly investigated so far. A selected group of features is extracted from the depth information of 3D images, which are then used for classification using a quadratic Support Vector Machine. Despite class imbalance often present in medical image datasets, the proposed algorithm achieves a top geometric mean of 94.87%, comprising 100.00% sensitivity and 90.00% specificity, using only depth information for the detection of Melanomas. Such results show that potential gains can be achieved by extracting information from this often overlooked dimension, which provides more balanced results in terms of sensitivity and specificity than other settings.

Melanoma classification using light-Fields with morlet scattering transform and CNN: Surface depth as a valuable tool to increase detection rate

Medical image classification through learning-based approaches has been increasingly used, namely in the discrimination of melanoma. However, for skin lesion classification in general, such methods commonly rely on dermoscopic or other 2D-macro RGB images. This work proposes to exploit beyond conventional 2D image characteristics, by considering a third dimension (depth) that characterises the skin surface rugosity, which can be obtained from light-field images, such as those available in the SKINL2 dataset. To achieve this goal, a processing pipeline was deployed using a morlet scattering transform and a CNN model, allowing to perform a comparison between using 2D information, only 3D information, or both. Results show that discrimination between Melanoma and Nevus reaches an accuracy of 84.00, 74.00 or 94.00% when using only 2D, only 3D, or both, respectively. An increase of 14.29pp in sensitivity and 8.33pp in specificity is achieved when expanding beyond conventional 2D information by also using depth. When discriminating between Melanoma and all other types of lesions (a further imbalanced setting), an increase of 28.57pp in sensitivity and decrease of 1.19pp in specificity is achieved for the same test conditions. Overall the results of this work demonstrate significant improvements over conventional approaches.

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.

Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection

Malignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate. The most effective approach to targeted treatment is early diagnosis. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis and representation. They optimize the features design task, essential for an automatic approach on different types of images, including medical. In this paper, we adopted pretrained deep convolutional neural networks architectures for the image representation with purpose to predict skin lesion melanoma. Firstly, we applied a transfer learning approach to extract image features. Secondly, we adopted the transferred learning features inside an ensemble classification context. Specifically, the framework trains individual classifiers on balanced subspaces and combines the provided predictions through statistical measures. Experimental phase on datasets of skin lesion images is performed and results obtained show the effectiveness of the proposed approach with respect to state-of-the-art competitors.