Skin cancer (SC) is one of the most prevalent cancers worldwide. Clinical evaluation of skin lesions is necessary to assess the characteristics of the disease; however, it is limited by long timelines and variety in interpretation. As early and accurate diagnosis of SC is crucial to increase patient survival rates, machine-learning (ML) and deep-learning (DL) approaches have been developed to overcome these issues and support dermatologists. We present a systematic literature review of recent research on the use of machine learning to classify skin lesions with the aim of providing a solid starting point for researchers beginning to work in this area. A search was conducted in several electronic databases by applying inclusion/exclusion filters and for this review, only those documents that clearly and completely described the procedures performed and reported the results obtained were selected. Sixty-eight articles were selected, of which the majority use DL approaches, in particular convolutional neural networks (CNN), while a smaller portion rely on ML techniques or hybrid ML/DL approaches for skin cancer detection and classification. Many ML and DL methods show high performance as classifiers of skin lesions. The promising results obtained to date bode well for the not-too-distant inclusion of these techniques in clinical practice.

Digital pathology (DP) has begun to play a key role in the evaluation of liver specimens. Recent studies have shown that a workflow that combines DP and artificial intelligence (AI) applied to histopathology has potential value in supporting the diagnosis, treatment evaluation, and prognosis prediction of liver diseases. Here, we provide a systematic review of the use of this workflow in the field of hepatology. Based on the PRISMA 2020 criteria, a search of the PubMed, SCOPUS, and Embase electronic databases was conducted, applying inclusion/exclusion filters. The articles were evaluated by two independent reviewers, who extracted the specifications and objectives of each study, the AI tools used, and the results obtained. From the 266 initial records identified, 25 eligible studies were selected, mainly conducted on human liver tissues. Most of the studies were performed using whole-slide imaging systems for imaging acquisition and applying different machine learning and deep learning methods for image pre-processing, segmentation, feature extractions, and classification. Of note, most of the studies selected demonstrated good performance as classifiers of liver histological images compared to pathologist annotations. Promising results to date bode well for the not-too-distant inclusion of these techniques in clinical practice.

Malaria is a disease that affects millions of people worldwide with a consistent mortality rate. The light microscope examination is the gold standard for detecting infection by malaria parasites. Still, it is limited by long timescales and requires a high level of expertise from pathologists. Early diagnosis of this disease is necessary to achieve timely and effective treatment, which avoids tragic consequences, thus leading to the development of computer-aided diagnosis systems based on artificial intelligence (AI) for the detection and classification of blood cells infected with the malaria parasite in blood smear images. Such systems involve an articulated pipeline, culminating in the use of machine learning and deep learning approaches, the main branches of AI. Here, we present a systematic literature review of recent research on the use of automated algorithms to identify and classify malaria parasites in blood smear images. Based on the PRISMA 2020 criteria, a search was conducted using several electronic databases including PubMed, Scopus, and arXiv by applying inclusion/exclusion filters. From the 606 initial records identified, 135 eligible studies were selected and analyzed. Many promising results were achieved, and some mobile and web applications were developed to address resource and expertise limitations in developing countries.

This paper illustrates how the penetration of electromagnetic waves in lossy media strongly depends on the waveform and not only on the media involved. In particular, the so-called inhomogeneous plane waves are compared against homogeneous plane waves illustrating how the first ones can generate deep penetration effects. Moreover, the paper provides examples showing how such waves may be practically generated. The approach taken here is analytical and it concentrates on the deep penetration conditions obtained by means of incident inhomogeneous plane waves incoming from a lossless medium and impinging on a lossy medium. Both conditions and constraints that the waveforms need to possess to achieve deep penetration are analysed. Some results are finally validated through numerical computations. The theory presented here is of interest in view of a practical implementation of the deep penetration effect.

Skin cancer (SC) is one of the most common cancers in the world and is a leading cause of death in humans. Melanoma (M) is the most aggressive form of skin cancer and has an increasing incidence rate. Early and accurate diagnosis of M is critical to increase patient survival rates; however, its clinical evaluation is limited by the long timelines, variety of interpretations, and difficulty in distinguishing it from nevi (N) because of striking similarities. To overcome these problems and to support dermatologists, several machine-learning (ML) and deep-learning (DL) approaches have been developed. In the proposed work, melanoma detection, understood as an anomaly detection task with respect to the normal condition consisting of nevi, is performed with the help of a convolutional neural network (CNN) along with the handcrafted texture features of the dermoscopic images as additional input in the training phase. The aim is to evaluate whether the preprocessing and segmentation steps of dermoscopic images can be bypassed while maintaining high classification performance. Network training is performed on the ISIC2018 and ISIC2019 datasets, from which only melanomas and nevi are considered. The proposed network is compared with the most widely used pre-trained networks in the field of dermatology and shows better results in terms of classification and computational cost. It is also tested on the ISIC2016 dataset to provide a comparison with the literature: it achieves high performance in terms of accuracy, sensitivity, and specificity.

In this paper, we exploit the enhanced penetration reachable through inhomogeneous waves to induce hyperthermia in biological tissues. We will present a leaky-wave antenna inspired by the Menzel antenna which has been shortened through opportune design and optimizations and that has been designed to optimize the penetration at the interface with the skin, allowing penetration in the skin layer at a constant temperature, and enhanced penetration in the overall structure considered. Past papers both numerically and analytically demonstrated the possibility of reducing the attenuation that the electromagnetic waves are subject to when travelling inside a lossy medium by using inhomogeneous waves. In those papers, a structure (the leaky-wave antenna) is shown to allow the effect, but such a radiator suffers from low efficiency. Also, at the frequencies that are most used for hyperthermia application, a classical leaky-wave antenna would be too long; here is where the idea of the shortened leaky-wave arises. To numerically analyze the penetration in biological tissues, this paper considers a numerical prototype of a sample of flesh, composed of superficial skin layers, followed by fat and an undefined layer of muscles.