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 21 publications

(6 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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“…Comparatively, only a few works are reported regarding the usage of optimization algorithms as a transformation technique. For example, the Crow search optimization algorithm is used was a transformation technique for improving the classification performance of weighted KNN in the severity classification of breast cancer [40].…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma

Nagarajan,

Chakravarthy,

Venkatesan

et al. 2023

Diagnostics

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One of the most prevalent cancers is oral squamous cell carcinoma, and preventing mortality from this disease primarily depends on early detection. Clinicians will greatly benefit from automated diagnostic techniques that analyze a patient’s histopathology images to identify abnormal oral lesions. A deep learning framework was designed with an intermediate layer between feature extraction layers and classification layers for classifying the histopathological images into two categories, namely, normal and oral squamous cell carcinoma. The intermediate layer is constructed using the proposed swarm intelligence technique called the Modified Gorilla Troops Optimizer. While there are many optimization algorithms used in the literature for feature selection, weight updating, and optimal parameter identification in deep learning models, this work focuses on using optimization algorithms as an intermediate layer to convert extracted features into features that are better suited for classification. Three datasets comprising 2784 normal and 3632 oral squamous cell carcinoma subjects are considered in this work. Three popular CNN architectures, namely, InceptionV2, MobileNetV3, and EfficientNetB3, are investigated as feature extraction layers. Two fully connected Neural Network layers, batch normalization, and dropout are used as classification layers. With the best accuracy of 0.89 among the examined feature extraction models, MobileNetV3 exhibits good performance. This accuracy is increased to 0.95 when the suggested Modified Gorilla Troops Optimizer is used as an intermediary layer.

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

Cited by 21 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

A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma

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