Neural Network Diagnosis of Malignant Melanoma from Color Images

Malignant melanoma is the deadliest form of all skin cancers. Approximately 32,000 new cases of malignant melanoma were diagnosed in 1991 in the United States, with approximately 80% of patients expected to survive 5 years. Fortunately, if detected early, even malignant melanoma may be treated successfully, Thus, in recent years, there has been rising interest in the automated detection and diagnosis of skin cancer, particularly malignant melanoma. Here, the authors present a novel neural network approach for the automated separation of melanoma from 3 benign categories of tumors which exhibit melanoma-like characteristics. The approach uses discriminant features, based on tumor shape and relative tumor color, that are supplied to an artificial neural network for classification of tumor images as malignant or benign. With this approach, for reasonably balanced training/testing sets, the authors are able to obtain above 80% correct classification of the malignant and benign tumors on real skin tumor images

Automatic Color Segmentation of Images with Application to Detection of Variegated Coloring in Skin Tumors

A description is given of a computer vision system, developed to serve as the front-end of a medical expert system, that automates visual feature identification for skin tumor evaluation. The general approach is to create different software modules that detect the presence or absence of critical features. Image analysis with artificial intelligence (AI) techniques, such as the use of heuristics incorporated into image processing algorithms, is the primary approach. On a broad scale, this research addressed the problem of segmentation of a digital image based on color information. The algorithm that was developed to segment the image strictly on the basis of color information was shown to be a useful aid in the identification of tumor border, ulcer, and other features of interest. As a specific application example, the method was applied to 200 digitized skin tumor images to identify the feature called variegated coloring. Extensive background information is provided, and the development of the algorithm is described

Identification of Variegated Coloring in Skin Tumors: Neural Network vs. Rule-Based Induction Methods

The use of neural networks for automatic identification of variegated coloring, which is believed to be one of the most predictive features for malignant melanoma, is described. The Nestor development system (NDS) was chosen for neural network implementation. At the heart of NDS is a three-layer neural network called a restricted Coulomb energy (RCE) network. The learning scheme and the database for detection of variegated coloring are discussed. Results are reporte

Performance of AI Methods in Detecting Melanoma

This research has shown that features extracted from color skin tumor images by computer vision methods can be reliable discriminators of malignant tumors from benign ones. Reliability was demonstrated by the monotonically increasing success ratios with increasing training set size and by the small standard deviations from the mean success rates. An average success rate of 70 percent in diagnosing melanoma was attained for a training set size of 60 percent. The presence or absence of atypical moles in the training and test sets was shown to have a dramatic impact on the effectiveness of the generated classification rules. This was the case with both AIM and lst-Class, and indicates a high potential for success if a method can be found for discriminating between atypical moles and melanom

Detection of Skin Tumor Boundaries in Color Images

A simple and yet effective method for finding the borders of tumors is presented as an initial step towards the diagnosis of skin tumors from their color images. The method makes use of an adaptive color metric from the red, green, and blue planes that contains information for discriminating the tumor from the background. Using this suitable coordinate transformation, the image is segmented. The tumor portion is then extracted from the segmented image and borders are drawn. Experimental results that verify the effectiveness of this approach are give

Novel Block Diagonalization for Reducing Features and Computations in Medical Diagnosis

Author's accepted manuscript.Available from 28/11/2021.acceptedVersio

Neural Network Diagnosis of Malignant Melanoma from Color Images

Malignant melanoma is the deadliest form of all skin cancers. Approximately 32,000 new cases of malignant melanoma were diagnosed in 1991, with approximately 80 percent of patients expected to survive five years [1]. Fortunately, if detected early, even malignant melanoma may be treated successfully. Thus, in recent years, there has been a rising interest in the automated detection and diagnosis of skin cancer, particularly malignant melanoma [2]. In this paper, we present a novel neural network approach for the automated separation of melanoma from three other benign categories of tumors which exhibit melanoma-like characteristics. Our approach is based on devising new and discriminant features which are used as inputs to an artificial neural network for classification of tumor images as malignant or benign. We have obtained promising results using our method on real skin cancer images

"Is a picture of a bird a bird": Policy recommendations for dealing with ambiguity in machine vision models

Many questions that we ask about the world do not have a single clear answer, yet typical human annotation set-ups in machine learning assume there must be a single ground truth label for all examples in every task. The divergence between reality and practice is stark, especially in cases with inherent ambiguity and where the range of different subjective judgments is wide. Here, we examine the implications of subjective human judgments in the behavioral task of labeling images used to train machine vision models. We identify three primary sources of ambiguity arising from (i) depictions of labels in the images, (ii) raters' backgrounds, and (iii) the task definition. On the basis of the empirical results, we suggest best practices for handling label ambiguity in machine learning datasets

Evolution and challenges in the design of computational systems for triage assistance

AbstractCompared with expert systems for specific disease diagnosis, knowledge-based systems to assist decision making in triage usually try to cover a much wider domain but can use a smaller set of variables due to time restrictions, many of them subjective so that accurate models are difficult to build. In this paper, we first study criteria that most affect the performance of systems for triage assistance. Such criteria include whether principled approaches from machine learning can be used to increase accuracy and robustness and to represent uncertainty, whether data and model integration can be performed or whether temporal evolution can be modeled to implement retriage or represent medication responses. Following the most important criteria, we explore current systems and identify some missing features that, if added, may yield to more accurate triage systems

ITK y VTK : Ingeniería inversa y análisis de arquitectura Pipeline

Proyecto de Trabajo de Grado en donde se utilizó una versión modificada de la metodología RUP para realizar un proceso de investigación a partir de un proceso de ingeniería inversa en donde se identificó la arquitectura de pipelines de las librerías de procesamiento de imágenes ITK y VTK. Así mismo, se realizó un prototipo de una interfaz para la edición de pipelines que aporte al desarrollo de flujo de datos para la creación de nuevos algoritmos de procesamiento.Degree work project where a modified version of the RUP methodology was used to do an investigation where a reverse engineer process was used to identify the pipeline architecture of the image processing libraries ITK and VTK. At the same time, a prototype of a user interface for the edition of pipelines was done to contribute in the development of data flows for the creation of new processing algorithms.Ingeniero (a) de SistemasPregrad