Introduction: Breast cancer is the most common cancer in women and one of the major causes of death from cancer among female around the world. The early detection and treatment are the major way to healing. The use of mammary thermography in Mastology is increasing as a complementary imaging technique to early detect lesions. Its use as a screening exam to identify breast disorders has been investigated. The aim of this study is to investigate the behavior of different classification methods while grouping the thermographic images into specific types of lesions. Methods: To evaluate our proposal, we built classifiers based on artificial neural networks, decision trees, Bayesian classifiers, and Haralick and Zernike attributes. The image database is composed by thermographic images acquired at the University Hospital of the Federal University of Pernambuco. These images are clinically classified into the classes cyst, malignant and benign. Moments of Zernike and Haralick were used as attributes.Results: Extreme Learning Machines (ELM) and Multilayer Perceptron networks (MLP) proved to be quite efficient classifiers for classification of breast lesions in thermographic images. Using 75% of the database for training, the maximum value obtained for accuracy was 73.38%, with a Kappa index of 0.6007. This result indicated to a sensitivity of 78% and specificity of 88%. The overall efficiency of the system was 83%. Conclusion: ELM showed to be a promising classifier to be used in the differentiation of breast lesions in thermographic images, due to its low computational cost and robustness.Keywords Breast cancer early diagnosis, Thermographic images, Mammary thermography, Artificial neural networks, Extreme learning machines.This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.How to cite this article: Santana MA, Pereira JMS, Silva FL, Lima NM, Sousa FN, Arruda GMS, Lima RCF, Silva WWA, Santos WP. Breast cancer diagnosis based on mammary thermography and extreme learning machines. Res Biomed Eng. 2018; 34(1):45-53.

Purpose Diagnosis and treatment in psychiatry are still highly dependent on reports from patients and on clinician judgment. This fact makes them prone to memory and subjectivity biases. As for other medical fields, where objective biomarkers are available, there has been an increasing interest in the development of such tools in psychiatry. To this end, vocal acoustic parameters have been recently studied as possible objective biomarkers, instead of otherwise invasive and costly methods. Patients suffering from different mental disorders, such as major depressive disorder (MDD), may present with alterations of speech. These can be described as uninteresting, monotonous, and spiritless speech and low voice. Methods Thirty-three individuals (11 males) over 18 years old were selected, 22 of which being previously diagnosed with MDD and 11 healthy controls. Their speech was recorded in naturalistic settings, during a routine medical evaluation for psychiatric patients, and in different environments for healthy controls. Voices from third parties were removed. The recordings were submitted to a vocal feature extraction algorithm, and to different machine learning classification techniques. Results The results showed that random tree models with 100 trees provided the greatest classification performances. It achieved mean accuracy of 87.5575% ± 1.9490, mean kappa index, sensitivity, and specificity of 0.7508 ± 0.0319, 0.9149 ± 0.0204, and 0.8354 ± 0.0254, respectively, for the detection of MDD. Conclusion The use of machine learning classifiers with vocal acoustic features appears to be very promising for the detection of major depressive disorder in this exploratory study, but further experiments with a larger sample will be necessary to validate our findings.

Purpose Psychiatry still needs objective biomarkers. In the context of schizophrenia, there are speech abnormalities such as tangentiality, derailment, alogia, neologisms, poverty of speech, and aprosodia. There is a growing interest in speech signals features as possible indicators of schizophrenia. This article aims to develop an intelligent tool for detection of schizophrenia using vocal patterns and machine learning techniques. The main advantages of this type of solution are the low cost, high performance, and for being non-invasive. Methods Thirty-one individuals over 18 years old were selected, 20 with previous diagnosis of schizophrenia, and 11 healthy controls. Their speech was audio-recorded in naturalistic settings, during a routine medical assessment for psychiatric patients. In the case of healthy patients, the recordings were made in different environments. Recordings were pre-processed, excluding non-participant voices. We extracted 33 features. We used the particle swarm optimization algorithm for feature selection. Results The classifiers' performance was analyzed with four metrics: accuracy, sensibility, specificity, and kappa index. Best results were achieved when considering all 33 extracted features. Within machine models, support vector machines (SVM) models provided the greatest classification performance, with mean accuracy of 91.76% for PUK kernel. Our results outperform those from most studies published so far for the detection of schizophrenia based on acoustic patterns. Conclusion The use of machine learning classifiers using vocal parameters, in particular SVM, has shown to be very promising for the detection of schizophrenia. Nevertheless, further experiments with a larger sample will be necessary to validate our findings.

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Purpose Diagnosis and treatment in psychiatry are still highly dependent on reports from patients and on clinician judgement. This fact makes them prone to memory and subjectivity biases. As for other medical fields, where objective biomarkers are available, there has been an increasing interest in the development of such tools in psychiatry. To this end, vocal acoustic parameters have been recently studied as possible objective biomarkers, instead of otherwise invasive and costly methods. Patients suffering from different mental disorders, such as major depressive disorder (MDD), may present with alterations of speech. These can be described as uninteresting, monotonous and spiritless speech, low voice. Methods Thirty-three individuals (11 males) over 18 years old were selected, 22 of which being previously diagnosed with MDD, and 11 healthy controls. Their speech was recorded in naturalistic settings, during a routine medical evaluation for psychiatric patients, and in different environments for healthy controls. Voices from third parties were removed. The recordings were submitted to to a vocal feature extraction algorithm, and to different machine learning classification techniques. Results The results showed that support vector machines (SVM) models provided the greatest classification performances for different kernels, with PUK kernel providing accuracy of 89.14% for the detection of MDD. Conclusion The use of machine learning classifiers with vocal acoustics features has shown to be very promising for the detection of major depressive disorder, but further tests with a larger sample will be necessary to validate our findings.

Breast cancer is the leading cause of death among women worldwide. Early detection and early treatment are critical to minimize the effects of this disease. In this sense, breast thermography has been explored in the process of diagnosing this type of cancer. Furthermore, in an attempt to optimize the diagnosis, intelligent pattern recognition techniques are being used. Features selection performs an essential task in this process to optimize these intelligent techniques. This chapter proposes a features selection method using Dialectical Optimization Method (ODM) associated to a KNN classifier. The authors found that this combination proved to be a good approach showing a low impact on breast lesion classification performance. They obtained around 5% decrease in accuracy, with a reduction of about 46.80% of the features vector. The specificity and sensitivity values they found were competitive to other widely used methods.

In this chapter, the authors used autoencoder in data preprocessing step in an attempt to improve image representation, consequently increasing classification performance. The authors applied autoencoder to the task of breast lesion classification in mammographic images. Image Retrieval in Medical Applications (IRMA) database was used. This database has a total of 2,796 ROI (regions of interest) images from mammograms. The images are from patients in one of the three conditions: with a benign lesion, a malignant lesion, or presenting healthy breast. In this study, images were from mostly fatty breasts and authors assessed different intelligent algorithms performance in grouping the images in their respective diagnosis.