The incidence of skin cancer is increasing and nurses are in an ideal position to help patients prevent and identify the disease at an early stage.

Prescribing in Dermatology: Skin cancer

Analysis of skin lesion images via visual inspection and manual examination to diagnose skin cancer has always been cumbersome This manual examination of skin lesions in order to detect melanoma can be time-consuming and tedious With the advancement in technology and rapid increase in computational resources, various machine learning techniques and deep learning models have emerged for the analysis of medical images most especially the skin lesion images The results of these models have been impressive, however analysis of skin lesion images with these techniques still experiences some challenges due to the unique and complex features of the skin lesion images This work presents a comprehensive survey of techniques that have been used for detecting skin cancer from skin lesion images The paper is aimed to provide an up-to-date survey that will assist investigators in developing efficient models that automatically and accurately detects melanoma from skin lesion images The paper is presented in five folds: First, we identify the challenges in detecting melanoma from skin lesions Second, we discuss the pre-processing and segmentation techniques of skin lesion images Third, we make comparative analysis of the state-of-the-arts Fourth we discuss classification techniques for classifying skin lesions into different classes of skin cancer We finally explore and analyse the performance of the state-of-the-arts methods employed in popular skin lesion image analysis competitions and challenges of ISIC 2018 and 2019 Application of ensemble deep learning models on well pre-processed and segmented images results in better classification performance of the skin lesion images

Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art

Skin Lesion detection and classification are very critical in diagnosing skin malignancy. Existing Deep learning-based Computer-aided diagnosis (CAD) methods still perform poorly on challenging skin lesions with complex features such as fuzzy boundaries, artifacts presence, low contrast with the background and, limited training datasets. They also rely heavily on a suitable turning of millions of parameters which often leads to over-fitting, poor generalization, and heavy consumption of computing resources. This study proposes a new framework that performs both segmentation and classification of skin lesions for automated detection of skin cancer. The proposed framework consists of two stages: the first stage leverages on an encoder-decoder Fully Convolutional Network (FCN) to learn the complex and inhomogeneous skin lesion features with the encoder stage learning the coarse appearance and the decoder learning the lesion borders details. Our FCN is designed with the sub-networks connected through a series of skip pathways that incorporate long skip and short-cut connections unlike, the only long skip connections commonly used in the traditional FCN, for residual learning strategy and effective training. The network also integrates the Conditional Random Field (CRF) module which employs a linear combination of Gaussian kernels for its pairwise edge potentials for contour refinement and lesion boundaries localization. The second stage proposes a novel FCN-based DenseNet framework that is composed of dense blocks that are merged and connected via the concatenation strategy and transition layer. The system also employs hyper-parameters optimization techniques to reduce network complexity and improve computing efficiency. This approach encourages feature reuse and thus requires a small number of parameters and effective with limited data. The proposed model was evaluated on publicly available HAM10000 dataset of over 10000 images consisting of 7 different categories of diseases with 98% accuracy, 98.5% recall, and 99% of AUC score respectively.

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FCN-Based DenseNet Framework for Automated Detection and Classification of Skin Lesions in Dermoscopy Images

Melanoma remains the most harmful form of skin cancer. Convolutional neural network (CNN) based classifiers have become the best choice for melanoma detection in the recent era. The research has indicated that classifiers based on CNN classify skin cancer images equivalent to dermatologists, which has allowed a quick and life-saving diagnosis. This study provides a systematic literature review of the latest research on melanoma classification using CNN. We restrict our study to the binary classification of melanoma. In particular, this research discusses the CNN classifiers and compares the accuracies of these classifiers when tested on non-published datasets. We conducted a systematic review of existing literature, identifying the literature through a systematic search of the IEEE, Medline, ACM, Springer, Elsevier, and Wiley databases. A total of 5112 studies were identified out of which 55 well-reputed studies were selected. The main objective of this study is to collect state of the art research which identify the recent research trends, challenges and opportunities for melanoma diagnosis and investigate the existing solutions for the diagnosis of melanoma detection using deep learning. Moreover, proposed taxonomy for melanoma detection has been presented that summarizes the broad variety of existing melanoma detection solutions. Lastly, proposed model, challenges and opportunities have been presented which helps the researchers in the domain of melanoma detection.

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Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities

The variation in skin textures and injuries, as well as the detection and classification of skin cancer, is a difficult task. Manually detecting skin lesions from dermoscopy images is a difficult and time-consuming process. Recent advancements in the domains of the internet of things (IoT) and artificial intelligence for medical applications demonstrated improvements in both accuracy and computational time. In this paper, a new method for multiclass skin lesion classification using best deep learning feature fusion and an extreme learning machine is proposed. The proposed method includes five primary steps: image acquisition and contrast enhancement; deep learning feature extraction using transfer learning; best feature selection using hybrid whale optimization and entropy-mutual information (EMI) approach; fusion of selected features using a modified canonical correlation based approach; and, finally, extreme learning machine based classification. The feature selection step improves the system’s computational efficiency and accuracy. The experiment is carried out on two publicly available datasets, HAM10000 and ISIC2018. The achieved accuracy on both datasets is 93.40 and 94.36 percent. When compared to state-of-the-art (SOTA) techniques, the proposed method’s accuracy is improved. Furthermore, the proposed method is computationally efficient.

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Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine

In this paper, a new Computer-Aided Detection (CAD) system for the detection and classification of dangerous skin lesions (melanoma type) is presented, through a fusion of handcraft features related to the medical algorithm ABCD rule (Asymmetry Borders-Colors-Dermatoscopic Structures) and deep learning features employing Mutual Information (MI) measurements. The steps of a CAD system can be summarized as preprocessing, feature extraction, feature fusion, and classification. During the preprocessing step, a lesion image is enhanced, filtered, and segmented, with the aim to obtain the Region of Interest (ROI); in the next step, the feature extraction is performed. Handcraft features such as shape, color, and texture are used as the representation of the ABCD rule, and deep learning features are extracted using a Convolutional Neural Network (CNN) architecture, which is pre-trained on Imagenet (an ILSVRC Imagenet task). MI measurement is used as a fusion rule, gathering the most important information from both types of features. Finally, at the Classification step, several methods are employed such as Linear Regression (LR), Support Vector Machines (SVMs), and Relevant Vector Machines (RVMs). The designed framework was tested using the ISIC 2018 public dataset. The proposed framework appears to demonstrate an improved performance in comparison with other state-of-the-art methods in terms of the accuracy, specificity, and sensibility obtained in the training and test stages. Additionally, we propose and justify a novel procedure that should be used in adjusting the evaluation metrics for imbalanced datasets that are common for different kinds of skin lesions.

https://www.mdpi.com/1099-4300/22/4/484/pdf

Melanoma and Nevus Skin Lesion Classification Using Handcraft and Deep Learning Feature Fusion via Mutual Information Measures

/pdf/an-intelligent-system-for-the-diagnosis-of-skin-cancer-on-1moty7l89k.pdf

An intelligent system for the diagnosis of skin cancer on digital images taken with dermoscopy

Gait analysis is one of the most important challenging research areas in clinical and computing settings. Gait biomechanics and gait human recognition are two major areas of interest. Alterations in walking can cause physical and metal health problems in people, so diagnoses and treatments derived from optimal gait analysis are very useful in clinical settings. This paper surveys the gait analysis methods, applications and platforms, gait biomechanics, as well as, gait recognition approaches, and datasets. Then, we describe contributions in gait forward kinematics, useful to assess gaits such as crouched and normal. Also, a framework for antalgic gait recognition based on human activity, using the gyroscope embedded in a smartphone is described. Different algorithms and metrics were used to perform the gait recognition, highlighting Support Vector Machines, Naive Bayes, k- Nearest Neighbours, and Accuracy and F-measure, respectively. Finally, we discuss the challenges and future perspectives on gait recognition.

Heydy Castillejos-Fernández

Papers

Melanoma and Nevus Skin Lesion Classification Using Handcraft and Deep Learning Feature Fusion via Mutual Information Measures

An intelligent system for the diagnosis of skin cancer on digital images taken with dermoscopy

Human gait analysis based on automatic recognition: A review