Lung Cancer Classification and Prediction Using Machine Learning and Image Processing

Nageswaran, Sharmila; Arunkumar, G; Bisht, Anil Kumar; Mewada, Shivlal; Kumar, Jagdish; Jawarneh, Malik; Asenso, Evans

doi:10.1155/2022/1755460

Cited by 74 publications

(19 citation statements)

References 29 publications

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“…Compared to random testing, machine learning improved detection of patients with NTMLD by thousand-fold with AUC of 0.94. (Nageswaran, et al 2022 ; Gould, et al 2021 ; Nemlander et al 2022 ). Murat Aykanat et al ( 2020 ) have done comparison of various algorithms for classification of respiratory diseases with text and audio data.…”

Section: Literature Surveymentioning

confidence: 99%

RETRACTED ARTICLE: Performance evaluation of deep learning techniques for lung cancer prediction

Deepapriya¹,

Parasuraman

Nandakumar³

et al. 2023

Soft Comput

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Due to the increase in pollution, the number of deaths caused by lung disease is rising rapidly. It is essential to predict the disease in earlier stages by means of high-level knowledge and acquaintance. Deep learning-based lung cancer prediction plays a vital role in assisting the medical practioners for diagnosing lung cancer in earlier stage. Computer-Aided diagnosis is considered to bring a boost to the field of medicine by tying it to automated systems. In this research paper, several models are experimented by using chest X-ray image or CT scan as an input to detect a particular disease. This research work is carried out to identify the best performing deep learning techniques for lung disease prediction. The performance of the method is evaluated using various performance metrics, such as precision, recall, accuracy and Jaccard index.

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Section: Literature Surveymentioning

confidence: 99%

RETRACTED ARTICLE: Performance evaluation of deep learning techniques for lung cancer prediction

Deepapriya¹,

Parasuraman

Nandakumar³

et al. 2023

Soft Comput

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show abstract

“…The survey further assures that the dependency is improved from one systems operation and dataset to an independent computing unit. Approaches such as machine learning [23] and classification [24] provide justifiable decision-making capabilities on the lung cancer computation. These approaches further customize and process the behavioral model of computations algorithms [25,26].…”

Section: Advanced Modelsmentioning

confidence: 99%

Ensemble Federated Learning Approach for Diagnostics of Multi-Order Lung Cancer

Subashchandrabose,

John,

Anbazhagu

et al. 2023

Diagnostics

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The early detection and classification of lung cancer is crucial for improving a patient’s outcome. However, the traditional classification methods are based on single machine learning models. Hence, this is limited by the availability and quality of data at the centralized computing server. In this paper, we propose an ensemble Federated Learning-based approach for multi-order lung cancer classification. This approach combines multiple machine learning models trained on different datasets allowing for improvising accuracy and generalization. Moreover, the Federated Learning approach enables the use of distributed data while ensuring data privacy and security. We evaluate the approach on a Kaggle cancer dataset and compare the results with traditional machine learning models. The results demonstrate an accuracy of 89.63% with lung cancer classification.

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“…The K-Means clustering algorithm [ 32 ] was chosen because of its maturity. An additional motivation is that this algorithm showed good results in other applications, such as image segmentation [ 33 ].…”

Section: Proposed Frameworkmentioning

confidence: 99%

A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning

Hayashi

Ruggiero

Estrella

et al. 2022

Sensors

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Robust, fault tolerant, and available systems are fundamental for the adoption of Internet of Things (IoT) in critical domains, such as finance, health, and safety. The IoT infrastructure is often used to collect a large amount of data to meet the business demands of Smart Cities, Industry 4.0, and Smart Home, but there is a opportunity to use these data to intrinsically monitor an IoT system in an autonomous way. A Test Driven Development (TDD) approach for automatic module assessment for ESP32 and ESP8266 IoT development devices based on unsupervised Machine Learning (ML) is proposed to monitor IoT device status. A framework consisting of business drivers, non-functional requirements, engineering view, dynamic system evaluation, and recommendations phases is proposed to be used with the TDD development tool. The proposal is evaluated in academic and smart home study cases with 25 devices, consisting of 15 different firmware versions collected in one week, with a total of over 550,000 IoT status readings. The K-Means algorithm was applied to free memory available, internal temperature, and Wi-Fi level metrics to automatically monitor the IoT devices under development to identify device constraints violation and provide insights for monitoring frequency configuration of different firmware versions. To the best of the authors’ knowledge, it is the first TDD approach for IoT module automatic assessment which uses machine learning based on the real testbed data. The IoT status monitoring and the Python scripts for model training and inference with K-Means algorithm are available under a Creative Commons license.

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Lung Cancer Classification and Prediction Using Machine Learning and Image Processing

Cited by 74 publications

References 29 publications

RETRACTED ARTICLE: Performance evaluation of deep learning techniques for lung cancer prediction

RETRACTED ARTICLE: Performance evaluation of deep learning techniques for lung cancer prediction

Ensemble Federated Learning Approach for Diagnostics of Multi-Order Lung Cancer

A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning

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