Detection and classification of masses in mammographic images in a multi-kernel approach

Lima, Sidney Marlon Lopes de; Silva-Filho, Abel G.; Santos, Wellington Pinheiro dos

doi:10.1016/j.cmpb.2016.04.029

Cited by 92 publications

(50 citation statements)

References 20 publications

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“…Machine learning techniques have been used in several tasks including medical image classification (Azevedo et al 2015;Barbosa et al 2020;Cordeiro et al 2016Cordeiro et al , 2017de Lima et al 2014de Lima et al , 2016de Santana et al 2018;de Vasconcelos et al 2018;Lima et al 2015;Pereira et al 2020a, b, c;Rodrigues et al 2019;Santana et al 2020;Silva et al 2020). Thus, these techniques can provide a secure and automatic way to diagnose COVID-19 in chest X-ray images.…”

Section: Introductionmentioning

confidence: 99%

IKONOS: an intelligent tool to support diagnosis of COVID-19 by texture analysis of X-ray images

et al. 2020

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Purpose In late 2019, the SARS-CoV-2 virus spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia. The standard diagnostic method for pneumonia is chest X-ray image. There are many advantages to using COVID-19 diagnostic X-rays: low cost, fast, and widely available. Methods We propose an intelligent system to support diagnosis by X-ray images. We tested Haralick and Zernike moments for feature extraction. Experiments with classic classifiers were done. Results Support vector machines stood out, reaching an average accuracy of 89.78%, average sensitivity of 0.8979, and average precision and specificity of 0.8985 and 0.9963, respectively. Conclusion Using features based on textures and shapes combined with classical classifiers, the developed system was able to differentiate COVID-19 from viral and bacterial pneumonia with low computational cost.

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Section: Introductionmentioning

confidence: 99%

IKONOS: an intelligent tool to support diagnosis of COVID-19 by texture analysis of X-ray images

et al. 2020

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“…These difficulties can be tackled by computational psychiatry, which applies machine learning (ML) with focus on clinical applications and single-subject treatments (Bzdok and Meyer-lindenberg 2018;Petzschner et al 2017). Machine learning has successful implementations in problem-solving tasks in several medical fields, like supportive diagnostic tools based on neuroanatomical structures for Alzheimer's disease (dos Santos et al 2009; W. P. dos Santos et al 2007), breast cancer (Cruz et al 2018;de Lima et al 2016;de Santana et al 2018), and multiple sclerosis diagnosis (Commowick et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Vocal acoustic analysis and machine learning for the identification of schizophrenia

et al. 2020

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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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“…The back propagation network is used to determine the presence of cancer. S. M. L. de Lima et al [16], proposed a method for detecting and classifying breast lesions using feature extraction based on the Calculation of Zernike Moments from a series of multi-resolution image components obtained by the series of wavelets. Chaghari et al [17], presented a new method to detect the mass in the mammogram based on cellular learning automata algorithm.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to the Detection of Mammogram Boundary

Rmili¹,

Moutaouakkil²,

Saleck³

et al. 2018

IJECE

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Mammography is a method used for the detection of breast cancer. computer-aided diagnostic (CAD) systems help the radiologist in the detection and interpretation of mass in breast mammography. One of the important information of a mass is its contour and its form because it provides valuable information about the abnormality of a mass. The accuracy in the recognition of the shape of a mass is related to the accuracy of the detected mass contours. In this work we propose a new approach for detecting the boundaries of lesion in mammography images based on region growing algorithm without using the threshold, the proposed method requires an initial rectangle surrounding the lesion selected manually by the radiologist (Region Of Interest), where the region growing algorithm applies on lines segments that attach each pixel of this rectangle with the seed point, such as the ends (seeds) of each line segment grow in a direction towards one another. The proposed approach is evaluated on a set of data with 20 masses of the MIAS base whose contours are annotated manually by expert radiologists. The performance of the method is evaluated in terms of specificity, sensitivity, accuracy and overlap. All the findings and details of approach are presented in detail.

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Detection and classification of masses in mammographic images in a multi-kernel approach

Cited by 92 publications

References 20 publications

IKONOS: an intelligent tool to support diagnosis of COVID-19 by texture analysis of X-ray images

IKONOS: an intelligent tool to support diagnosis of COVID-19 by texture analysis of X-ray images

Vocal acoustic analysis and machine learning for the identification of schizophrenia

A New Approach to the Detection of Mammogram Boundary

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