Recent advances in airborne and spaceborne hyperspectral imaging technology have provided end users with rich spectral, spatial, and temporal information. They have made a plethora of applications feasible for the analysis of large areas of the Earth?s surface. However, a significant number of factors-such as the high dimensions and size of the hyperspectral data, the lack of training samples, mixed pixels, light-scattering mechanisms in the acquisition process, and different atmospheric and geometric distortions-make such data inherently nonlinear and complex, which poses major challenges for existing methodologies to effectively process and analyze the data sets. Hence, rigorous and innovative methodologies are required for hyperspectral image (HSI) and signal processing and have become a center of attention for researchers worldwide.

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Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Hyperspectral image classification has been a vibrant area of research in recent years. Given a set of observations, i.e., pixel vectors in a hyperspectral image, classification approaches try to allocate a unique label to each pixel vector. However, the classification of hyperspectral images is a challenging task for a number of reasons, such as the presence of redundant features, the imbalance among the limited number of available training samples, and the high dimensionality of the data.

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Advanced Spectral Classifiers for Hyperspectral Images: A review

Melanoma is the deadliest form of skin cancer. While curable with early detection, only highly trained specialists are capable of accurately recognizing the disease. As expertise is in limited supply, automated systems capable of identifying disease could save lives, reduce unnecessary biopsies, and reduce costs. Toward this goal, we propose a system that combines recent developments in deep learning with established machine learning approaches, creating ensembles of methods that are capable of segmenting skin lesions, as well as analyzing the detected area and surrounding tissue for melanoma detection. The system is evaluated using the largest publicly available benchmark dataset of dermoscopic images, containing 900 training and 379 testing images. New state-of-the-art performance levels are demonstrated, leading to an improvement in the area under receiver operating characteristic curve of 7.5% (0.843 versus 0.783), in average precision of 4% (0.649 versus 0.624), and in specificity measured at the clinically relevant 95% sensitivity operating point 2.9 times higher than the previous state of the art (36.8% specificity compared to 12.5%). Compared to the average of eight expert dermatologists on a subset of 100 test images, the proposed system produces a higher accuracy (76% versus 70.5%), and specificity (62% versus 59%) evaluated at an equivalent sensitivity (82%).

Deep learning ensembles for melanoma recognition in dermoscopy images

Background: State-of-the-art classifiers based on convolutional neural networks (CNNs) were shown to classify images of skin cancer on par with dermatologists and could enable lifesaving and fast diagnoses, even outside the hospital via installation of apps on mobile devices. To our knowledge, at present there is no review of the current work in this research area. Objective: This study presents the first systematic review of the state-of-the-art research on classifying skin lesions with CNNs. We limit our review to skin lesion classifiers. In particular, methods that apply a CNN only for segmentation or for the classification of dermoscopic patterns are not considered here. Furthermore, this study discusses why the comparability of the presented procedures is very difficult and which challenges must be addressed in the future. Methods: We searched the Google Scholar, PubMed, Medline, ScienceDirect, and Web of Science databases for systematic reviews and original research articles published in English. Only papers that reported sufficient scientific proceedings are included in this review. Results: We found 13 papers that classified skin lesions using CNNs. In principle, classification methods can be differentiated according to three principles. Approaches that use a CNN already trained by means of another large dataset and then optimize its parameters to the classification of skin lesions are the most common ones used and they display the best performance with the currently available limited datasets. Conclusions: CNNs display a high performance as state-of-the-art skin lesion classifiers. Unfortunately, it is difficult to compare different classification methods because some approaches use nonpublic datasets for training and/or testing, thereby making reproducibility difficult. Future publications should use publicly available benchmarks and fully disclose methods used for training to allow comparability.

Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review.

This work presents an approach for melanoma recognition in dermoscopy images that combines deep learning, sparse coding, and support vector machine SVM learning algorithms. One of the beneficial aspects of the proposed approach is that unsupervised learning within the domain, and feature transfer from the domain of natural photographs, eliminates the need of annotated data in the target task to learn good features. The applied feature transfer also allows the system to draw analogies between observations in dermoscopic images and observations in the natural world, mimicking the process clinical experts themselves employ to describe patterns in skin lesions. To evaluate the methodology, performance is measured on a dataset obtained from the International Skin Imaging Collaboration, containing 2624 clinical cases of melanoma 334, atypical nevi 144, and benign lesions 2146. The approach is compared to the prior state-of-art method on this dataset. Two-fold cross-validation is performed 20 times for evaluation 40 total experiments, and two discrimination tasks are examined: 1 melanoma vs. all non-melanoma lesions, and 2 melanoma vs. atypical lesions only. The presented approach achieves an accuracy of 93.1% 94.9% sensitivity, and 92.8% specificity for the first task, and 73.9% accuracy 73.8% sensitivity, and 74.3% specificity for the second task. In comparison, prior state-of-art ensemble modeling approaches alone yield 91.2% accuracy 93.0% sensitivity, and 91.0% specificity first the first task, and 71.5% accuracy 72.7% sensitivity, and 68.9% specificity for the second. Differences in performance were statistically significant p $$<$$ 0.05, suggesting the proposed approach is an effective improvement over prior state-of-art.

Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images

Malignant melanoma is the third most frequent type of skin cancer and one of the most malignant tumors, accounting
for 79% of skin cancer deaths. Melanoma is highly curable if diagnosed early and treated properly as survival rate varies
between 15% and 65% from early to terminal stages, respectively. So far, melanoma diagnosis is depending subjectively
on the dermatologist's expertise. Computer-aided diagnosis (CAD) systems based on epiluminescense light microscopy
can provide an objective second opinion on pigmented skin lesions (PSL). This work systematically analyzes the
evidence of the effectiveness of automated melanoma detection in images from a dermatoscopic device. Automated
CAD applications were analyzed to estimate their diagnostic outcome. Searching online databases for publication dates
between 1985 and 2011, a total of 182 studies on dermatoscopic CAD were found. With respect to the systematic
selection criterions, 9 studies were included, published between 2002 and 2011. Those studies formed databases of
14,421 dermatoscopic images including both malignant "melanoma" and benign "nevus", with 8,110 images being
available ranging in resolution from 150 x 150 to 1568 x 1045 pixels. Maximum and minimum of sensitivity and
specificity are 100.0% and 80.0% as well as 98.14% and 61.6%, respectively. Area under the receiver operator
characteristics (AUC) and pooled sensitivity, specificity and diagnostics odds ratio are respectively 0.87, 0.90, 0.81, and
15.89. So, although that automated melanoma detection showed good accuracy in terms of sensitivity, specificity, and
AUC, but diagnostic performance in terms of DOR was found to be poor. This might be due to the lack of
dermatoscopic image resources (ground truth) that are needed for comprehensive assessment of diagnostic performance.
In future work, we aim at testing this hypothesis by joining dermatoscopic images into a unified database that serves as
a standard reference for dermatology related research in PSL classification.

A systematic review of automated melanoma detection in dermatoscopic images and its ground truth data

https://doi.org/10.1016/j.neucom.2022.01.055

A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images

Skin lesion border irregularity is considered an important clinical feature for the early diagnosis of melanoma, representing the B feature in the ABCD rule In this article we propose an automated approach for skin lesion border irregularity detection The approach involves extracting the skin lesion from the image, detecting the skin lesion border, measuring the border irregularity, training a Convolutional Neural Network and Gaussian naive Bayes ensemble, to the automatic detection of border irregularity, which results in an objective decision on whether the skin lesion border is considered regular or irregular The approach achieves outstanding results, obtaining an accuracy, sensitivity, specificity, and F-score of 936%, 100%, 925% and 961%, respectively

A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images

We describe our methods that achieved the 3rd and 4th places in tasks 1 and 2, respectively, at ISIC challenge 2019. The goal of this challenge is to provide the diagnostic for skin cancer using images and meta-data. There are nine classes in the dataset, nonetheless, one of them is an outlier and is not present on it. To tackle the challenge, we apply an ensemble of classifiers, which has 13 convolutional neural networks (CNN), we develop two approaches to handle the outlier class and we propose a straightforward method to use the meta-data along with the images. Throughout this report, we detail each methodology and parameters to make it easy to replicate our work. The results obtained are in accordance with the previous challenges and the approaches to detect the outlier class and to address the meta-data seem to be work properly.

Skin cancer detection based on deep learning and entropy to detect outlier samples

In land cover assessment, classes often gradually change from one to another. Therefore, it is difficult to allocate sharp boundaries between different classes of interest. To overcome this issue and model such conditions, fuzzy techniques that resemble human reasoning have been proposed as alternatives. Fuzzy C-means is the most common fuzzy clustering technique, but its concept is based on a local search mechanism and its convergence rate is rather slow, especially considering high-dimensional problems (e.g., in processing of hyperspectral images). Here, in order to address those shortcomings of hard approaches, a new approach is proposed, i.e., fuzzy C-means which is optimized by fractional order Darwinian particle swarm optimization. In addition, to speed up the clustering process, the histogram of image intensities is used during the clustering process instead of the raw image data. Furthermore, the proposed clustering approach is combined with support vector machine classification to accurately classify hyperspectral images. The new classification framework is applied on two well-known hyperspectral data sets; Indian Pines and Salinas. Experimental results confirm that the proposed swarm-based clustering approach can group hyperspectral images accurately in a time-efficient manner compared to other existing clustering techniques.

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Abder-Rahman Ali

Papers

A systematic review of automated melanoma detection in dermatoscopic images and its ground truth data

A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images

A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images

Skin cancer detection based on deep learning and entropy to detect outlier samples

A Novel Evolutionary Swarm Fuzzy Clustering Approach for Hyperspectral Imagery