A heuristic approach for multiple instance learning by linear separation

Fuduli, Antonio; Gaudioso, Manlio; Khalaf, Walaa; Vocaturo, Eugenio

doi:10.1007/s00500-021-06713-1

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Cited by 7 publications

References 26 publications

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Fast Walsh–Hadamard transform and deep learning approach for diagnosing psychiatric diseases from electroencephalography (EEG) signals

Göker,

Tosun

2023

Neural Comput & Applic

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Fast Walsh–Hadamard transform and deep learning approach for diagnosing psychiatric diseases from electroencephalography (EEG) signals

Göker,

Tosun

2023

Neural Comput & Applic

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A comparative study of linear type multiple instance learning techniques for detecting COVID-19 by chest X-ray images

Avolio,

Fuduli,

Vocaturo

et al. 2024

Prog Artif Intell

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At the end of 2019, the World Health Organization (WHO) referred that the Public Health Commission of Hubei Province, China, reported cases of severe and unknown pneumonia. A new coronavirus, SARS-CoV-2, was identified as responsible for the lung infection, called COVID-19 (COronaVIrus Disease 2019). Although the definitive COVID-19 diagnosis is made through specific molecular tests, an early diagnosis by imaging became crucial to contain the spread, morbidity and mortality of the pandemic. In such context, chest X-ray radiography, as an element that assists the diagnosis allowing also the follow-up of the disease, plays a very important role since it is the most easily available and least expensive alternative. This work focuses on applying different linear type instance-level Multiple Instance Learning techniques to discriminate between COVID-19 and common viral pneumonia chest X-ray images, which is a difficult task due to the strong similarity characterizing the two classes. A relevant advantage of such approaches is that they are also suitable in terms of interpretability, as they easily allow clinicians to identify abnormal subregions in a positive radiographic image. Numerical experiments have been performed on a set of 200 images, obtaining the following results: accuracy = 95%, sensitivity = 99.29%, specificity = 91.24% and MCC = 0.9. The used algorithms appear promising in practical applications, taking into account their high speed and considering that no particular pre-processing techniques have been employed.

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A comparative performance assessment of artificial intelligence based classifiers and optimized feature reduction technique for breast cancer diagnosis

Batool,

Zainab

2024

Computers in Biology and Medicine

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A heuristic approach for multiple instance learning by linear separation

Cited by 7 publications

References 26 publications

Fast Walsh–Hadamard transform and deep learning approach for diagnosing psychiatric diseases from electroencephalography (EEG) signals

Fast Walsh–Hadamard transform and deep learning approach for diagnosing psychiatric diseases from electroencephalography (EEG) signals

A comparative study of linear type multiple instance learning techniques for detecting COVID-19 by chest X-ray images

A comparative performance assessment of artificial intelligence based classifiers and optimized feature reduction technique for breast cancer diagnosis

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