Classification of the Lung Diseases from CT Scans by Advanced Segmentation Techniques using Genetic Algorithm

Bhuvaneswari, C.; Aruna, P.; Loganathan, D.

doi:10.5120/13568-1389

Cited by 6 publications

(6 citation statements)

References 18 publications

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“…Wearable devices with cloud storage [50,51] and data analytics are also used to indicate fall of any one or two specific medical issues related to chronic disease [44]. For example, wearable devices can indicate heart condition and initiate an emergency call in the case that a heart attack is detected [36,37].…”

Section: Structured Datamentioning

confidence: 99%

“…The connectivity in IoT enables communication among a variety of objects in a faster mechanism, being linked to the local and remote networks [16]. Thus, smart environments and wearable devices [37] are found to be more appealing in the health industry for the patients of all ages. The recent market studies state that there are numerous wrist watches / bands that are available in the market.…”

Section: Priya and Jinnymentioning

confidence: 99%

“…They used Natural Language Processing (NLP) and text-mining techniques for the conversion of unstructured data into a structured format. Zia Uddin et al [37] proposed a multimodal human activity disease prediction system using multiple wearable sensors. The obtained sensor data are then encoded to model time-sequential information using Recurrent Neural Network (RNN) algorithm.…”

Section: Unstructured Datamentioning

confidence: 99%

“…The most common input datasets considered for the development of chronic disease prediction models are the structured datasets. In certain studies [13] structured as well as unstructured dataset (Electronic Health Record (EHR), medical images and gene data) are considered, whereas few studies [37,44] used sensor data like Electrocardiogram (ECG), magnetometer, accelerometer, gyroscope and so on. It is observed that very few studies use feature selection methods for further processing of prediction models.…”

Section: Comparative Study Of Chronic Disease Prediction For Elderly People Using Traditional and Modern ML Techniquesmentioning

confidence: 99%

“…Among these, the CNN-MDRP proved better by performance measurements, compared to the CNN-UDRP. In addition, RNN algorithm [37] was used for the training of the model, and obtained an accuracy of 99.69 %. Another study [30] made use of the CART algorithm for the purpose of training, achieving an accuracy of nearly 92%.…”

Section: Priya and Jinnymentioning

confidence: 99%

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Elderly Healthcare System for Chronic Ailments using Machine Learning Techniques – a Review

Priya

Jinny

2021

eijs

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World statistics declare that aging has direct correlations with more and more health problems with comorbid conditions. As healthcare communities evolve with a massive amount of data at a faster pace, it is essential to predict, assist, and prevent diseases at the right time, especially for elders. Similarly, many researchers have discussed that elders suffer extensively due to chronic health conditions. This work was performed to review literature studies on prediction systems for various chronic illnesses of elderly people. Most of the reviewed papers proposed machine learning prediction models combined with, or without, other related intelligence techniques for chronic disease detection of elderly patients at an early stage to avoid emergency situations. This method provides a promising approach in the analysis of either structured or unstructured datasets to produce very substantial pattern discoveries. By defining the generic architecture for the prediction model, we reviewed various papers involved in similar fields, based on suggested methodologies and their associated outcomes. The study discussed the pros and cons of different prediction models using traditional and modern machine learning techniques.

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Section: Structured Datamentioning

confidence: 99%

Section: Priya and Jinnymentioning

confidence: 99%

Section: Unstructured Datamentioning

confidence: 99%

Section: Comparative Study Of Chronic Disease Prediction For Elderly People Using Traditional and Modern ML Techniquesmentioning

confidence: 99%

Section: Priya and Jinnymentioning

confidence: 99%

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Elderly Healthcare System for Chronic Ailments using Machine Learning Techniques – a Review

Priya

Jinny

2021

eijs

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An efficient lung image classification and detection using spiral‐optimized Gabor filter with convolutional neural network

Sivakumar,

Yogesh,

Vatchala

et al. 2023

Int J Imaging Syst Tech

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Lung cancer has a high death rate of around seven million cases every year worldwide. A computed tomography (CT) scan provides certain essential data concerning lung diseases and their diagnosis. The main objective of this work is to classify various lung diseases such as Normal, Bronchiectasis, and Pleural Effusion. The proposed approach consists of three stages, namely pre‐processing, feature extraction, and classification. At first, CT lung images are collected from the dataset and pre‐processed. After pre‐processing, the important texture features are extracted from each image. For feature extraction, spiral‐optimized Gabor filter (SOGF) is utilized. The proposed SOGF is a combination of spiral optimization algorithm (SOA) and Gabor filter (GF). Then, the extracted features are given to the convolutional neural network (CNN) to classify different types of lung diseases. For comparison, we use different classifiers, namely artificial neural network (ANN), Random Tree, and the Naïve Bayes. The experimental results show that our proposed approach attained the maximum accuracy of 93.67% which is high compared to other methods.

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Comparative Analysis Between Different Lung Segmentation Techniques

Bhosle¹,

Kanade²,

Bhosale³

et al. 2023

ITM Web Conf.

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For the purpose of identifying various lung illnesses, computed tomography (CT) pictures of the lung must be segmented. The most significant aspect of medical imaging is image segmentation. Via an automated process, the ROI (region of interest) is extracted. The process of segmentation separates an image into sections according to a particular interest, such as segmenting human organs or tissue. Several medical disorders can benefit from the segmented image of the lung. We specifically compared and analysed various threshold segmentation algorithms in this paper in an effort to determine which one would be the best to use moving forward with image processing. We have used Computed Tomography (CT) images of Lungs with Tuberculosis (TB) dataset from Kaggle for image processing and compared them with finely masked CT images to infer the best Threshold algorithm. We have decided to do the analysis on Threshold algorithms named as Binary Threshold, Otsu’s Threshold, and Adaptive Threshold. Comparison has been done based on performance parameters such as Accuracy, Precision, Recall Value, f1-score, etc. The results are also represented in Graphical format for better understanding of performed comparison study.

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Classification of the Lung Diseases from CT Scans by Advanced Segmentation Techniques using Genetic Algorithm

Cited by 6 publications

References 18 publications

Elderly Healthcare System for Chronic Ailments using Machine Learning Techniques – a Review

Elderly Healthcare System for Chronic Ailments using Machine Learning Techniques – a Review

An efficient lung image classification and detection using spiral‐optimized Gabor filter with convolutional neural network

Comparative Analysis Between Different Lung Segmentation Techniques

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