Automated skin lesion analysis for simultaneous detection and recognition is still challenging inter-class homogeneity intra-class heterogeneity, leading to low generic capability of a single convolutional neural network (CNN) with limited datasets. This article proposes an end-to-end deep CNN-based framework the lesions, named Dermo-DOCTOR, consisting two encoders. The feature maps from encode...

1Department of Chemical and Biomolecular Engineering, University of California Berkeley, Berkeley, CA, USA, 2Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea, 3Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA, 4 Computational Research Division, Lawrence Berkeley National Lab...

This paper presents a system for medical image diagnosis that uses transfer learning (TL) and feature selection techniques. The main aim of TL on pre-trained models such as MobileNetV3 is to extract features from raw images. Here, novel optimization algorithm called the Artificial Hummingbird Algorithm based Aquila Optimization (AHA-AO) proposed. AHA-AO used select only most relevant ensure imp...

Measuring the use of theory that informs empirical research demonstrates rigour in a discipline. It also identifies key theories and connections to other disciplines. This study focuses on theory, broadly defined, information behaviour. Full-length papers from ISIC conference proceedings were analysed. Kumasi et al.’s (2013) talk framework was adapted for determining level use.Analysis. Content...

This paper describes the participation of Araguaia Medical Vision Lab at the International Skin Imaging Collaboration 2017 Skin Lesion Challenge. We describe the use of deep convolutional neural networks in attempt to classify images of Melanoma and Seborrheic Keratosis lesions. With use of finetuned GoogleNet and AlexNet we attained results of 0.950 and 0.846 AUC on Seborrheic Keratosis and Me...

Convolutional neural networks (CNNs) have been broadly employed in dermoscopic image analysis, mainly as a result of the large amount data gathered by International Skin Imaging Collaboration (ISIC). As many other medical imaging domains, state-of-the-art methods take advantage architectures developed for tasks, frequently assuming full transferability between enormous sets natural images (e.g....

Yu Zhiyong a,d, E. Mielczarski b, J.A. Mielczarski b, D. Laub c, L. Kiwi-Minsker a, A. Renken a, J. Kiwi a,∗ a Institute of Chemical Sciences and Engineering, SB-ISIC-LGRC, Swiss Federal Institute of Technology (EPFL), Station 6, 1015 Lausanne, Switzerland b INPL/ENG, CNRS, UMR 7569, LEM, Pole de l’Eau, 15 Av du Charmois, 54501 Vandoeuvre les-Nancy, France c Institute of Electron Microscopy (CI...

Abstract Human Skin cancer is commonly detected visually through clinical screening followed by a dermoscopic examination. However, automated skin lesion classification remains challenging due to the visual similarities between benign and melanoma lesions. In this work, authors proposed new Artificial Intelligence-Based method classify method, we used Residual Deep Convolution Neural Network. W...