The International Skin Imaging Collaboration (ISIC) datasets have become a leading repository for researchers in machine learning for medical image analysis, especially in the field of skin cancer detection and malignancy assessment. They contain tens of thousands of dermoscopic photographs together with gold-standard lesion diagnosis metadata. The associated yearly challenges have resulted in major contributions to the field, with papers reporting measures well in excess of human experts. Skin cancers can be divided into two major groups - melanoma and non-melanoma. Although less prevalent, melanoma is considered to be more serious as it can quickly spread to other organs if not treated at an early stage. In this paper, we summarise the usage of the ISIC dataset images and present an analysis of yearly releases over a period of 2016 - 2020. Our analysis found a significant number of duplicate images, both within and between the datasets. Additionally, we also noted duplicates spread across testing and training sets. Due to these irregularities, we propose a duplicate removal strategy and recommend a curated dataset for researchers to use when working on ISIC datasets. Given that ISIC 2020 focused on melanoma classification, we conduct experiments to provide benchmark results on the ISIC 2020 test set, with additional analysis on the smaller ISIC 2017 test set. Testing was completed following the application of our duplicate removal strategy and an additional data balancing step. As a result of removing 14,310 duplicate images from the training set, our benchmark results show good levels of melanoma prediction with an AUC of 0.80 for the best performing model. As our aim was not to maximise network performance, we did not include additional steps in our experiments. Finally, we provide recommendations for future research by highlighting irregularities that may present research challenges. A list of image files with reference to the original ISIC dataset sources for the recommended curated training set will be shared on our GitHub repository (available at www.github.com/mmu-dermatology-research/isic_duplicate_removal_strategy).

One of the biggest challenge in the field of deep learning is the parameter selection and optimization process. In recent years different algorithms have been proposed including bio-inspired solutions to solve this problem, however, there are many challenges including local minima, saddle points, and vanishing gradients. In this paper, we introduce the Whale Optimisation Algorithm (WOA) based on the swarm foraging behavior of humpback whales to optimise neural network hyperparameters. We wish to stress that to the best of our knowledge this is the first attempt that uses Whale Optimisation Algorithm for the optimisation task of hyperparameters. After a detailed description of the WOA algorithm we formulate and explain the application in deep learning, present the implementation, and compare the proposed algorithm with other well-known algorithms including widely used Grid and Random Search methods. Additionally, we have implemented a third dimension feature analysis to the original WOA algorithm to utilize 3D search space (3D-WOA). Simulations show that the proposed algorithm can be successfully used for hyperparameters optimization, achieving accuracy of 89.85% and 80.60% for Fashion MNIST and Reuters datasets, respectively.

Over the past two decades, malignant melanoma incidence rate has dramatically risen but melanoma mortality has only recently stabilized. Due to its propensity to metastasize and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Thickness is one of the most important factor in melanoma prognosis and it is used to establish the size of the surgical margin, as well as to select patients for sentinel lymph node biopsy. However, little work has concentrated on the evaluation of melanoma thickness both from the clinical as well as computer-aided diagnostic side. To address this problem, we propose an effective computer-vision based machine learning tool that can perform the preoperative evaluation of melanoma thickness. The novelty of our approach is that we directly predict the thickness of the skin lesion into one of three classes: less than 0.75 mm, 0.76-1.5 mm, and greater that 1.5 mm. In this study, we use transfer learning of the pre-trained, adapted to our application VGG-19 convolutional neural network (CNN) with an adjusted densely-connected classifier. Due to the limited data we investigate the transfer learning method where we apply knowledge from model trained on a different task. Our database contains 244 dermoscopy images. Experiments confirm the developed algorithm's ability to classify skin lesion thickness with 87.2% overall accuracy what is a state-of-the-art result in melanoma thickness prediction.

Tissue segmentation in whole-slide images is an important task in digital pathology, required for efficient and accurate computer-aided diagnostics. Precise tissue segmentation is particularly significant for a correct diagnosis in cases, when tissue structure of a specimen is very porous, such as skin specimens. In this paper, we addressed the problem of fore- and background segmentation in histopatological images of skin specimens stained with hematoxylin and eosin (H&E), which has not been solved yet, by a novel method based on a combination of statistical analysis, color thresholding, and binary morphology. We validated our algorithm on large extracts from 60 high-resolution whole slide images, with differing staining quality and captured under varying imaging conditions, from three laboratories. The size of extracts varies from 2000×1500 to 20000×30000 pixels and the number of images used in our study matches the number of H&E images used by other research teams. We compared our method to the published ones (GrabCut and FESI) and showed that our approach outperforms its counterparts (Jaccard index of 0.929 vs. 0.776 and 0.695).

Nowadays, researchers use vision-based measurement tools to record, detect, and monitor an atmospheric phenomenon called haze. It impedes the proper functioning of many outdoor industrial systems, such as autonomous driving, surveillance, satellite imagery, etc. Conventional visibility restoration methods cannot accurately recover image quality due to inaccurate estimations of haze thickness and the presence of color-cast effects. Deep neural networks are evolving due to their ability to directly dehaze images from hazy scenes. Therefore, a unique attention-based end-to-end dehazing network named Oval-Net has been proposed in this study to restore clear images from its counterpart without employing the atmospheric scattering model. The Oval-Net is an encoder-decoder architecture that uses spatial and channel attention at each stage to focus on dominant and significant information while avoiding the transmission of irrelevant information from the encoder to the decoder, allowing quicker convergence. The proposed approach outperforms seven state-of-the-art algorithms in quantitative and qualitative assessments of a variety of synthetic and real-world hazy images, proving its effectiveness for vision-based industrial systems.

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.