Computer aided diagnostic support system for skin cancer: A review of techniques and algorithms

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided. © 2013 Ammara Masood and Adel Ali Al-Jumaily

Computer Aided Diagnostic Support System for Skin cancer: Review of techniques and algorithms

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique’s performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided

Resolution invariant wavelet features of melanoma studied by SVM classifiers

This article refers to the Computer Aided Diagnosis of the melanoma skin cancer. We derive wavelet-based features of melanoma from the dermoscopic images of pigmental skin lesions and apply binary C-SVM classifiers to discriminate malignant melanoma from dysplastic nevus. The aim of this research is to select the most efficient model of the SVM classifier for various image resolutions and to search for the best resolution-invariant wavelet bases. We show AUC as a function of the wavelet number and SVM kernels optimized by the Bayesian search for two independent data sets. Our results are compatible with the previous experiments to discriminate melanoma in dermoscopy images with ensembling and feed-forward neural networks

Predicting the Clinical Management of Skin Lesions Using Deep Learning

Automated machine learning approaches to skin lesion diagnosis from images are approaching dermatologist-level performance. However, current machine learning approaches that suggest management decisions rely on predicting the underlying skin condition to infer a management decision without considering the variability of management decisions that may exist within a single condition. We present the first work to explore image-based prediction of clinical management decisions directly without explicitly predicting the diagnosis. In particular, we use clinical and dermoscopic images of skin lesions along with patient metadata from the Interactive Atlas of Dermoscopy dataset (1011 cases; 20 disease labels; 3 management decisions) and demonstrate that predicting management labels directly is more accurate than predicting the diagnosis and then inferring the management decision (13.73±3.93% and 6.59±2.86% improvement in overall accuracy and AUROC respectively), statistically significant at p<0.001. Directly predicting management decisions also considerably reduces the over-excision rate as compared to management decisions inferred from diagnosis predictions (24.56% fewer cases wrongly predicted to be excised). Furthermore, we show that training a model to also simultaneously predict the seven-point criteria and the diagnosis of skin lesions yields an even higher accuracy (improvements of 4.68±1.89% and 2.24±2.04% in overall accuracy and AUROC respectively) of management predictions. Finally, we demonstrate our model’s generalizability by evaluating on the publicly available MClass-D dataset and show that our model agrees with the clinical management recommendations of 157 dermatologists as much as they agree amongst each other

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 245)

This bibliography lists 363 reports, articles and other documents introduced into the NASA scientific and technical information system in April 1983

Aerospace Medicine and Biology: 1983 cumulative index

This publication is a cumulative index to the abstracts contained in the Supplements 242 through 253 of Aerospace Medicine and Biology: A Continuing Bibliography. It includes six indexes--subject, personal author, corporate source, contract number, report number, and accession number

Kernel Methods and Measures for Classification with Transparency, Interpretability and Accuracy in Health Care

Support vector machines are a popular method in machine learning. They learn from data about a subject, for example, lung tumors in a set of patients, to classify new data, such as, a new patient’s tumor. The new tumor is classified as either cancerous or benign, depending on how similar it is to the tumors of other patients in those two classes—where similarity is judged by a kernel. The adoption and use of support vector machines in health care, however, is inhibited by a perceived and actual lack of rationale, understanding and transparency for how they work and how to interpret information and results from them. For example, a user must select the kernel, or similarity function, to be used, and there are many kernels to choose from but little to no useful guidance on choosing one. The primary goal of this thesis is to create accurate, transparent and interpretable kernels with rationale to select them for classification in health care using SVM—and to do so within a theoretical framework that advances rationale, understanding and transparency for kernel/model selection with atomic data types. The kernels and framework necessarily co-exist. The secondary goal of this thesis is to quantitatively measure model interpretability for kernel/model selection and identify the types of interpretable information which are available from different models for interpretation. Testing my framework and transparent kernels with empirical data I achieve classification accuracy that is better than or equivalent to the Gaussian RBF kernels. I also validate some of the model interpretability measures I propose

Fungal Pigments 2021

New edition of the reprint Fungal pigments: Chapters titles: PART 1. Investigation on various chemical classes of fungal pigments: Genomic Analysis and Assessment of Melanin Synthesis in Amorphotheca resinae by Jeong-Joo Oh et al.; Fungal Melanins and Applications in Healthcare, Bioremediation and Industry by Ellie Rose Mattoon et al.; Recent Findings in Azaphilone Pigments by Lúcia P. S. Pimenta et al.; Characterization of a Biofilm Bioreactor Designed for the Single-Step Production of Aerial Conidia and Oosporein by Beauveria bassiana PQ2 by Héctor Raziel Lara-Juache et al.; PART 2. Molecular characterization: Molecular Characterization of Fungal Pigments by Miriam S. Valenzuela-Gloria et al.; PART 3. Biological properties: Seven New Cytotoxic and Antimicrobial Xanthoquinodins from Jugulospora vestita by Lulu Shao et al.; PART 4. Toxicity assessment and safety evaluation of fungal pigments: Safety Evaluation of Fungal Pigments for Food Applications by Rajendran Poorniammal et al.; Preliminary Examination of the Toxicity of Spalting Fungal Pigments: A Comparison between Extraction Methods by Badria H. Almurshidi et al.; PART 5. Use of by-products or waste for industrial production of fungal pigments: Production of Bio-Based Pigments from Food Processing Industry By-Products Using Aspergillus carbonarius by Ezgi Bezirhan Arikan et al.; PART 6. Prospective aspects and brainstorming: Does Structural Color Exist in True Fungi? by Juliet Brodie et al.; Fungal Biomarkers Stability in Mars Regolith Analogues after Simulated Space and Mars-like Conditions by Alessia Cassaro et al