Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Review

Skin lesions known as naevi exhibit diverse characteristics such as size, shape, and colouration. The concept of an "Ugly Duckling Naevus" comes into play when monitoring for melanoma, referring to a lesion with distinctive features that sets it apart from other lesions in the vicinity. As lesions within the same individual typically share similarities and follow a predictable pattern, an ugly duckling naevus stands out as unusual and may indicate the presence of a cancerous melanoma. Computer-aided diagnosis (CAD) has become a significant player in the research and development field, as it combines machine learning techniques with a variety of patient analysis methods. Its aim is to increase accuracy and simplify decision-making, all while responding to the shortage of specialized professionals. These automated systems are especially important in skin cancer diagnosis where specialist availability is limited. As a result, their use could lead to life-saving benefits and cost reductions within healthcare. Given the drastic change in survival when comparing early stage to late-stage melanoma, early detection is vital for effective treatment and patient outcomes. Machine learning (ML) and deep learning (DL) techniques have gained popularity in skin cancer classification, effectively addressing challenges, and providing results equivalent to that of specialists. This article extensively covers modern Machine Learning and Deep Learning algorithms for detecting melanoma and suspicious naevi. It begins with general information on skin cancer and different types of naevi, then introduces AI, ML, DL, and CAD. The article then discusses the successful applications of various ML techniques like convolutional neural networks (CNN) for melanoma detection compared to dermatologists' performance. Lastly, it examines ML methods for UD naevus detection and identifying suspicious naevi

Classification of Frequency and Phase Encoded Steady State Visual Evoked Potentials for Brain Computer Interface Speller Applications using Convolutional Neural Networks

Over the past decade there have been substantial improvements in vision based Brain-Computer Interface (BCI) spellers for quadriplegic patient populations. This thesis contains a review of the numerous bio-signals available to BCI researchers, as well as a brief chronology of foremost decoding methodologies used to date. Recent advances in classification accuracy and information transfer rate can be primarily attributed to time consuming patient specific parameter optimization procedures. The aim of the current study was to develop analysis software with potential ‘plug-in-and-play’ functionality. To this end, convolutional neural networks, presently established as state of the art analytical techniques for image processing, were utilized. The thesis herein defines deep convolutional neural network architecture for the offline classification of phase and frequency encoded SSVEP bio-signals. Networks were trained using an extensive 35 participant open source Electroencephalographic (EEG) benchmark dataset (Department of Bio-medical Engineering, Tsinghua University, Beijing). Average classification accuracies of 82.24% and information transfer rates of 22.22 bpm were achieved on a BCI naïve participant dataset for a 40 target alphanumeric display, in absence of any patient specific parameter optimization

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Adopting Accessibility Guidelines for Videogames to Collectible Card Games

The development of accessible video games has been discussed through multiple publications in the 21st century; however, little to no attention has been given to non-electronic gaming. Like video games, Collectible Card Games (CCG) have also gained massive popularity, but no accessible guidelines have been created to help the disabled better play them. The need for inclusion in gaming is critical because it can act as a medium for social interaction and a learning tool for teaching. Today offers numerous technologies that can help those with disabilities, such as microcomputers, Artificial Intelligence (AI), Optical Character Recognition (OCR), and Text to Speech, can all be used to help those with disabilities. In this thesis, we create a proof of concept Accessible Technology to help those with low vision play CGG and show that guidelines that worked for video games can be brought over to help with CCG. We use a Raspberry Pi 4B and the Raspberry Pi HQ camera module, Scene Text Detection, OCR, and Text to Speech to read the cards of a CCG. This AT acts similar to a microscope where it captures the image of a card, finds the card’s name, feeds it to a database query, and reads the record from the database to the player. We performed several evaluations where the participant played a game of Yugioh, followed by answering a questionnaire. We found in these evaluations that the AT was primarily successful, and the user problems come from poor text to speech and participants having a hard time remembering or comprehending card information

Recent Changes in Drug Abuse Scenario: The Novel Psychoactive Substances (NPS) Phenomenon

copyright 2019 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.Final Published versio

多色配色の審美的評価に影響を与える知覚的特徴に対する実験的研究

早大学位記番号:新7946早稲田大