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 61 publications

(12 citation statements)

References 29 publications

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“…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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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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“…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%

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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“…13,39,40,43,51,52 In recent years, the technology of machine learning has made significant strides in processing images, including recognition, segmentation, and classification. 62 These technologies have been widely used and made significant contributions to the auxiliary diagnosis of organ lesions, especially in the lung, 63 breast, 64 thyroid, 65 and other organs. 62 Therefore, a number of researchers have begun also to apply traditional machine learning or deep learning technologies to identify HB in MASH and conduct quantitative analysis automatically.…”

Section: Analys Is Limitations Of E Xis Ting Hb Recog Niti On Alg Ori...mentioning

confidence: 99%

Object detection: A novel AI technology for the diagnosis of hepatocyte ballooning

Zheng,

Sha,

Deng

et al. 2023

Liver International

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Metabolic dysfunction‐associated fatty liver disease (MAFLD) has reached epidemic proportions worldwide and is the most frequent cause of chronic liver disease in developed countries. Within the spectrum of liver disease in MAFLD, steatohepatitis is a progressive form of liver disease and hepatocyte ballooning (HB) is a cardinal pathological feature of steatohepatitis. The accurate and reproducible diagnosis of HB is therefore critical for the early detection and treatment of steatohepatitis. Currently, a diagnosis of HB relies on pathological examination by expert pathologists, which may be a time‐consuming and subjective process. Hence, there has been interest in developing automated methods for diagnosing HB. This narrative review briefly discusses the development of artificial intelligence (AI) technology for diagnosing fatty liver disease pathology over the last 30 years and provides an overview of the current research status of AI algorithms for the identification of HB, including published articles on traditional machine learning algorithms and deep learning algorithms. This narrative review also provides a summary of object detection algorithms, including the principles, historical developments, and applications in the medical image analysis. The potential benefits of object detection algorithms for HB diagnosis (specifically those combined with a transformer architecture) are discussed, along with the future directions of object detection algorithms in HB diagnosis and the potential applications of generative AI on transformer architecture in this field. In conclusion, object detection algorithms have huge potential for the identification of HB and could make the diagnosis of MAFLD more accurate and efficient in the near future.

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Lung Cancer Classification and Prediction Using Machine Learning and Image Processing

Cited by 61 publications

References 29 publications

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

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

Performance evaluation of deep learning techniques for lung cancer prediction

Object detection: A novel AI technology for the diagnosis of hepatocyte ballooning

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