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

Subashchandrabose, Umamaheswaran; John, Rajan; Anbazhagu, Usha Veerasamy; Venkatesan, Vinoth Kumar; Thyluru Ramakrishna, Mahesh

doi:10.3390/diagnostics13193053

Cited by 13 publications

(3 citation statements)

References 32 publications

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“…Moreover, the MTL-MGAN technique provides a generic way to connect the target and source domains, suggesting that it may have wide applications in the LCD context. In [3], Umamaheswaran Subashchandrabose et al investigated the diagnostic efficacy of an ensemble federated learning strategy for multi-order lung cancer. The suggested approach combines many machine learning methods trained on various datasets to classify multi-order lung cancer using ensemble federated learning.…”

Section: Literature Reviewmentioning

confidence: 99%

Lrfe: A Novel Local Response Feature Elimination Process for Identification of Lung Cancer Cells

Chintha Vishnu Vardhana Reddy

2024

jes

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One of the main causes of cancer-related mortality across the globe is lung cancer. Early-stage lung cancer frequently exhibits no symptoms, which delays diagnosis until the illness has progressed. Before symptoms manifest, screening and early detection techniques can aid in the early diagnosis and treatment of lung cancer. Many olden research papers have implemented image processing and few latest papers have implemented computer vision techniques to detect lung cancer. Particularly when dealing with minor or subtle anomalies, image processing algorithms may not be able to detect lung cancer lesions with sufficient sensitivity and specificity. It is still difficult to increase the detection algorithms' accuracy and dependability, especially when dealing with early-stage lesions or situations where attributes overlap. It takes a lot of processing power, such as high-performance GPUs and enormous memory capacities, to train deep learning models, particularly large-scale convolutional neural networks (CNNs). In this proposed research, the model pre-processes the images using the ostu and sober filter mechanisms because Otsu's approach adjusts to the features of the input image, including noise, contrast, and lighting fluctuations. It is capable of handling images with varying dynamic ranges and intensity distributions without depending on pre-established threshold settings. When it comes to image noise, the Sobel filter is more resilient than other edge detection methods. It produces clearer edge maps and fewer false detections by determining the gradient magnitude, which amplifies edge information while suppressing noise. The features are extracted using the tuned AlexNet pre-trained model, in AlexNet there is a layer known as “Layer-wise Relevance Propagation”. By giving each pixel or feature in the input image a relevance score, the LRP layer offers fine-grained feature attribution. This makes it possible to analyze in great depth which particular elements or areas of the input image are most important for the network to forecast, which helps to clarify the underlying patterns that the network has learned. At last, the extracted features are further reduced using the enhanced feature elimination method. By iteratively selecting subsets of features based on their importance, RFE helps to identify the most relevant features for the classification task. Integrating RFE with SVM classifiers can lead to improved model performance by focusing on the subset of features that are most discriminative and informative for the classification problem.

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Section: Literature Reviewmentioning

confidence: 99%

Lrfe: A Novel Local Response Feature Elimination Process for Identification of Lung Cancer Cells

Chintha Vishnu Vardhana Reddy

2024

jes

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“…Another problem with bone X-ray images is that bone regions overlap with other organs and muscles. The joints between bones must also be considered in the segmentation process for better analysis [15,16].…”

Section: Related Workmentioning

confidence: 99%

Image Processing Using Feature-Based Segmentation Techniques for the Analysis of Medical Images

Ranjith,

Natarajan,

Madhuri

et al. 2023

RAiSE-2023

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“…For platforms focused on real-time and continuous monitoring of mental health problems, including depression, these trade-offs may be significant. Despite the fact that FL [ 16 , 17 ] has been used for a variety of healthcare services, there is a dearth of research that has utilized FL for privacy-preserving multimodal analysis to assess MDD.…”

Section: Introductionmentioning

confidence: 99%

Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram

Gupta,

Khullar,

Goyal

et al. 2023

Diagnostics

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In this day and age, depression is still one of the biggest problems in the world. If left untreated, it can lead to suicidal thoughts and attempts. There is a need for proper diagnoses of Major Depressive Disorder (MDD) and evaluation of the early stages to stop the side effects. Early detection is critical to identify a variety of serious conditions. In order to provide safe and effective protection to MDD patients, it is crucial to automate diagnoses and make decision-making tools widely available. Although there are various classification systems for the diagnosis of MDD, no reliable, secure method that meets these requirements has been established to date. In this paper, a federated deep learning-based multimodal system for MDD classification using electroencephalography (EEG) and audio datasets is presented while meeting data privacy requirements. The performance of the federated learning (FL) model was tested on independent and identically distributed (IID) and non-IID data. The study began by extracting features from several pre-trained models and ultimately decided to use bidirectional short-term memory (Bi-LSTM) as the base model, as it had the highest validation accuracy of 91% compared to a convolutional neural network and LSTM with 85% and 89% validation accuracy on audio data, respectively. The Bi-LSTM model also achieved a validation accuracy of 98.9% for EEG data. The FL method was then used to perform experiments on IID and non-IID datasets. The FL-based multimodal model achieved an exceptional training and validation accuracy of 99.9% when trained and evaluated on both IID and non-IIID datasets. These results show that the FL multimodal system performs almost as well as the Bi-LSTM multimodal system and emphasize its suitability for processing IID and non-IIID data. Several clients were found to perform better than conventional pre-trained models in a multimodal framework for federated learning using EEG and audio datasets. The proposed framework stands out from other classification techniques for MDD due to its special features, such as multimodality and data privacy for edge machines with limited resources. Due to these additional features, the framework concept is the most suitable alternative approach for the early classification of MDD patients.

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

Cited by 13 publications

References 32 publications

Lrfe: A Novel Local Response Feature Elimination Process for Identification of Lung Cancer Cells

Lrfe: A Novel Local Response Feature Elimination Process for Identification of Lung Cancer Cells

Image Processing Using Feature-Based Segmentation Techniques for the Analysis of Medical Images

Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram

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