Classification of some epidemics through microscopic images by using deep learning. Comparison

Abstract

In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.

An Experimental Study for Automatic Detection of COVID-19 from Chest CT Scans. A Comparison through Deep Learning and Support Vector Machine

“…The interesting issue about these results is that when the database is small or very unbalanced, the DL model learns less than the SVM model. We had already obtained similar results in another paper published in [12]. Note that the false positives (FP) and false negatives (FN) by the DL model were slightly higher.…”

“…In this paper, our goal is not to give an exhaustive explanation about SVM, which is a wellknown machine learning technique. In this case our database was not very unbalanced as in [12], but we proceeded in the same way. We selected the SVM method to carry out the comparison because it has proven its effectiveness and it was necessary to compare the obtained results with CNN with a classical machine learning method.…”

“…These parameters made the training process efficient, creating a good relation between training and model validation precision [22]. In this work, we followed a procedure very similar to the one shown in [12].…”

“…Many methods using machine learning have been proposed for bio-medical image analysis [8,9], and among them, deep learning has occupied a prominent position [10,11], as these algorithms extract image features automatically, making them more suitable for automated biomedical image analysis, as well as providing accurate diagnoses. Furthermore, deep learning is known to be classified into supervised and unsupervised learning, with supervised learning giving exceptional results in bio-medical image processing, with performance comparable to that of humans, and sometimes superior [12]. Supervised learning requires a set of real data (ground truth) and prior knowledge about the result to be obtained with that dataset.…”