Pneumonia Classification using Deep Learning in Healthcare

Abstract

There is a great growing interest in the domain of deep learning techniques for identifying and classifying images with various datasets. An enormous availability of datasets (e.g. ChestX-Ray14 dataset) has developed a keen interest in deep learning. Pneumonia is a disease that is caused by various bacteria, virus etc. X-ray is one of the major diagnosis tools for diagnosing pneumonia. This research work mainly proposes a convolutional neural system (CNN) model prepared without any preparation to group and identify the occurrence of pneumonia disease from a given assortment of chest X-ray image tests. Dissimilar to different strategies that depend exclusively on more learning draws near or conventional carefully assembled systems to accomplish an amazing grouping execution, and developed a convolutional neural arrange model without any preparation to separate and character the images to decide whether an individual is suffering with pneumonia. This model could help alleviate the dependability and difficult challenges frequently confronted to manage therapeutic problems. In this paper, CNN algorithm has been used along with different data augmentation techniques for improving the classification accuracies which has been discussed to increase the performance which will help in improving the validation and training accuracies and characterization of exactness of the CNN model and accomplished various results. This experiment was carried out using python language and has shown improved outcomes.

Coronavirus Pneumonia Classification Using X-Ray and CT Scan Images With Deep Convolutional Neural Network Models

“…The proposed work has been tested using chest X-ray & CT dataset. In the given study by (Verma & Prakash, 2020), it has been demonstrated that how one can classify the true and false cases of pneumonia easily from a small dataset of X-ray images using CNN approach along with different data augmentation techniques for improving the classification accuracies which will help in improving the validation and characterization of exactness of the CNN model. (Acharya & Satapathy, 2020) have proposed an automatic detection of pneumonia from chest radiography image using the deep Siamese based neural network.…”

“…During each epoch, a random augmentation of images that preserve collinearity and distance ratios was performed. According to (Verma & Prakash, 2020), this technique helps to improve and add some effective knowledge about the data for enhancing the classification accuracy of the images and better results. Besides that, the model trained with data augmentation is more robust and can generalize better.…”

Machine Learning based Prediction and Diagnosis of Heart Disease using multiple models

“…For detection the disease, many classi cation algorithms are employed to categorize patient data. [4] During training period, the classi cation model [5] is trained using information from the standard dataset.…”

Automatic Detection of Pneumonia using Concatenated Convolutional Neural Network

“…As for the intermediate stage, it started with the emergence of machine learning algorithms like support vector machine (SVM), k-nearest neighbor (KNN), Naïve bayes (NB), and random forest (RF) [11] [12]. The research in the current period i.e., the third stage focused on the use of deep learning models, speci cally convolutional neural network (CNN) models, such as VGGNetm ExNet, and GoogleNet [13] The rest of this paper is organized as follows: In the section 2, we present the most prominent previous studies, speci cally those that focused on deep learning models and supported their results with a performance curve. Section 3 presents the proposed model in detail.…”