Data Mining Practical Machine Learning Tools and Techniques

Detection of plant leaf diseases using image segmentation and soft computing techniques

Background: State-of-the-art classifiers based on convolutional neural networks (CNNs) were shown to classify images of skin cancer on par with dermatologists and could enable lifesaving and fast diagnoses, even outside the hospital via installation of apps on mobile devices. To our knowledge, at present there is no review of the current work in this research area. Objective: This study presents the first systematic review of the state-of-the-art research on classifying skin lesions with CNNs. We limit our review to skin lesion classifiers. In particular, methods that apply a CNN only for segmentation or for the classification of dermoscopic patterns are not considered here. Furthermore, this study discusses why the comparability of the presented procedures is very difficult and which challenges must be addressed in the future. Methods: We searched the Google Scholar, PubMed, Medline, ScienceDirect, and Web of Science databases for systematic reviews and original research articles published in English. Only papers that reported sufficient scientific proceedings are included in this review. Results: We found 13 papers that classified skin lesions using CNNs. In principle, classification methods can be differentiated according to three principles. Approaches that use a CNN already trained by means of another large dataset and then optimize its parameters to the classification of skin lesions are the most common ones used and they display the best performance with the currently available limited datasets. Conclusions: CNNs display a high performance as state-of-the-art skin lesion classifiers. Unfortunately, it is difficult to compare different classification methods because some approaches use nonpublic datasets for training and/or testing, thereby making reproducibility difficult. Future publications should use publicly available benchmarks and fully disclose methods used for training to allow comparability.

Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review.

An automated detection and classification of citrus plant diseases using image processing techniques: A review

Techniques and algorithms for computer aided diagnosis of pigmented skin lesions—A review

https://web.fe.up.pt/~tavares/downloads/publications/artigos/CMPB_2015_288.pdf

Computational methods for the image segmentation of pigmented skin lesions

Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given.

/pdf/computational-methods-for-pigmented-skin-lesion-43vobvh0zw.pdf

Computational methods for pigmented skin lesion classification in images: review and future trends

A discussion about the state of the art of computer-aided diagnosis methods.A new approach for identifying pigmented skin lesion features and types.The approach is based on asymmetry, border, colour and texture properties.It combines an anisotropic diffusion filter, an active contour model and an SVM.Experiments focused on skin lesion segmentation and classification. Skin cancer is considered one of the most common types of cancer in several countries and its incidence rate has increased in recent years. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. Computational analysis of skin lesion images has become a challenging research area due to the difficulty in discerning some types of skin lesions. A novel computational approach is presented for extracting skin lesion features from images based on asymmetry, border, colour and texture analysis, in order to diagnose skin lesion types. The approach is based on an anisotropic diffusion filter, an active contour model without edges and a support vector machine. Experiments were performed regarding the segmentation and classification of pigmented skin lesions in macroscopic images, with the results obtained being very promising.

https://repositorio-aberto.up.pt/bitstream/10216/83597/3/128722.pdf

A computational approach for detecting pigmented skin lesions in macroscopic images

There has been an alarming increase in the number of skin cancer cases worldwide in recent years, which has raised interest in computational systems for automatic diagnosis to assist early diagnosis and prevention. Feature extraction to describe skin lesions is a challenging research area due to the difficulty in selecting meaningful features. The main objective of this work is to find the best combination of features, based on shape properties, colour variation and texture analysis, to be extracted using various feature extraction methods. Several colour spaces are used for the extraction of both colour- and texture-related features. Different categories of classifiers were adopted to evaluate the proposed feature extraction step, and several feature selection algorithms were compared for the classification of skin lesions. The developed skin lesion computational diagnosis system was applied to a set of 1104 dermoscopic images using a cross-validation procedure. The best results were obtained by an optimum-path forest classifier with very promising results. The proposed system achieved an accuracy of 92.3%, sensitivity of 87.5% and specificity of 97.1% when the full set of features was used. Furthermore, it achieved an accuracy of 91.6%, sensitivity of 87% and specificity of 96.2%, when 50 features were selected using a correlation-based feature selection algorithm.

/pdf/computational-diagnosis-of-skin-lesions-from-dermoscopic-3h5f0wtk8g.pdf

Computational diagnosis of skin lesions from dermoscopic images using combined features

https://repositorio-aberto.up.pt/bitstream/10216/106154/3/203441.pdf

Roberta B. Oliveira

Papers

Computational methods for the image segmentation of pigmented skin lesions

Computational methods for pigmented skin lesion classification in images: review and future trends

A computational approach for detecting pigmented skin lesions in macroscopic images

Computational diagnosis of skin lesions from dermoscopic images using combined features

Skin lesion computational diagnosis of dermoscopic images: Ensemble models based on input feature manipulation.