Identification of mammary lesions in thermographic images: feature selection study using genetic algorithms and particle swarm optimization

Rodrigues, Amanda L.; Santana, Maíra Araújo de; Azevedo, Washington W.; Bezerra, Renato S.; Barbosa, Valter A. F.; Lima, Ricardo Carvalho; Santos, Wellington Pinheiro dos

doi:10.1007/s42600-019-00024-z

Cited by 32 publications

(17 citation statements)

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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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“…Machine learning is an intensive field of research, with successful applications to solve several problems in health sciences, like breast cancer diagnosis (Cordeiro et al 2012;Cordeiro et al 2016;de Lima et al 2016;Rodrigues et al 2019), Alzheimer's disease diagnosis support based on neuroanatomical features (dos Santos et al 2009;dos Santos et al 2008;dos Santos et al 2007), multiple sclerosis diagnosis (Commowick et al 2018), and many applied neuroscience solutions (da Silva Junior et al 2019;de Freitas et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Detection of major depressive disorder using vocal acoustic analysis and machine learning—an exploratory study

et al. 2020

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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.

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“…MLPs and other artificial neural networks architectures are commonly used in support diagnosis applications (Naraei et al, 2016), e.g. liver disease dianogis (Abdar et al, 2018), heart diasese diagnosis (Hasan et al, 2017), breast cancer diagnosis over breast thermography (de Vasconcelos et al, 2018; Pereira et al, 2020b; Santana et al, 2020; Pereira et al, 2020a,c; Santana et al, 2018; Rodrigues et al, 2019) and mammography images (de Lima et al, 2016; Lima et al, 2015; de Lima et al, 2014; Silva et al, 2020; Cordeiro et al, 2017, 2016; de Lima et al, 2014; Cruz et al, 2018), for recognition of intracranial epileptic seizures (Raghu & Sriraam, 2017), and multiple sclerosis diagnosis support (Commowick et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

Covid-19 rapid test by combining a random forest based web system and blood tests

Barbosa¹,

Gomes

Santana

et al. 2020

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The disease caused by the new type of coronavirus, the Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-Cov2 has already caused over 400 thousand deaths to date. The diagnosis of the disease has an important role in combating Covid-19. Proposed method In this work, we propose a web system, Heg.IA, which seeks to optimize the diagnosis of Covid-19 through the use of artificial intelligence. The main ideia is that healthcare professionals can insert 41 hematological parameters from common blood tests and arterial gasometry into the system. Then, Heg.IA will provide a diagnostic report. It will indicate if the patient is infected with SARS-Cov2 virus, and also predict the type of hospitalization (regular ward, semi-ICU, or ICU). We developed a web system called Heg.IA to support decision-making regarding to diagnosis of Covid-19 and to the indication of hospitalization on regular ward, semi-ICU or ICU. This application is based on decision trees in a Random Forest architecture with 90 trees. The system showed to be highly efficient, with great results for both Covid-19 diagnosis and to recommend hospitalization. For the first scenario we found average results of accuracy of 92.891% ± 0.851, kappa index of 0.858 ± 0.017, sensitivity of 0.936 ± 0.011, precision of 0.923 ± 0.011, specificity of 0.921 ± 0.012 and area under ROC of 0.984 ± 0.003. As for the indication of hospitalization, we achieved excellent performance of accuracies above 99% and more than 0.99 for the other metrics in all situations. By using a computationally simple method, based on the classical decision trees, we were able to achieve high diagnosis performance. Heg.IA system may be a way to overcome the testing unavailability in the context of Covid-19. We also expect the system will provide wide access to Covid-19 effective diagnosis and thereby reach and help saving lives.

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Identification of mammary lesions in thermographic images: feature selection study using genetic algorithms and particle swarm optimization

Cited by 32 publications

References 24 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

Detection of major depressive disorder using vocal acoustic analysis and machine learning—an exploratory study

Covid-19 rapid test by combining a random forest based web system and blood tests

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