Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Naseer, Iftikhar; Masood, Tehreem; Akram, Sheeraz; Jaffar, Arfan; Rashid, Muhammad; Iqbal, Muhammad Amjad

doi:10.32604/cmc.2023.032927

Cited by 21 publications

(11 citation statements)

References 44 publications

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“…A sizable dataset of 284,992,606 coronavirus-infected patients was used in the investigation. In [9], Iftikhar Naseer et al discussed the importance of lung cancer, a dangerous malignancy that has a high death rate. Based on an altered version of the AlexNet architecture, the researcherss presented the LungNet-SVM model, which showed impressive lung nodule detection accuracy.…”

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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“…In order to accurately and effectively diagnose lung cancer, the authors I. Naseer et al, presented the LungNet-SVM model for automated module identification technique in CT scans. On the LUNA16 dataset, the model demonstrated outstanding performance with 97.64% accuracy [30]. In the study by M Pradhan et al, a unique approach was constructed to automatically classify the LC25000 lung histology image collection.…”

Section: Related Workmentioning

confidence: 99%

An Explainable Method for Lung Cancer Detection and Localisation from Tissue Images through Convolutional Neural Networks

Mercaldo,

Tibaldi,

Lombardi

et al. 2024

Electronics

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Lung cancer, a prevalent and life-threatening condition, necessitates early detection for effective intervention. Considering the recent advancements in deep learning techniques, particularly in medical image analysis, which offer unparalleled accuracy and efficiency, in this paper, we propose a method for the automated identification of cancerous cells in lung tissue images. We explore various deep learning architectures with the objective of identifying the most effective one based on both quantitative and qualitative assessments. In particular, we assess qualitative outcomes by incorporating the concept of prediction explainability, enabling the visualization of areas within tissue images deemed relevant to the presence of lung cancer by the model. The experimental analysis, conducted on a dataset comprising 15,000 lung tissue images, demonstrates the effectiveness of our proposed method, yielding an accuracy rate of 0.99.

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“…The proposed method using histogram computation process improves the quality of image also eliminates the noise pixel present in the CT lung image. The modified U-Net based lobe segmentation and nodule detection model was utilized in [43] to proposed lung cancer classification. The detection model is applied to improve the efficiency and minimize the error rate.…”

Section: Literature Reviewmentioning

confidence: 99%

Residual Partial Derivative Multilayer Perceptron Deep Learning Based Classifier for Lung Cancer Detection

Padmanandam,

Khilar,

Pitla

et al. 2023

Preprint

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Lung cancer is a significant and frequent types of cancer globally both in patients and mortality rate. Computed Tomography (CT) test is efficient for early lung cancer detection due to potentiality in constructing lung images, generating nodules both in high resolution and tumor detection. But, early detection of lung cancer is obtained as difficult and time-consuming task. Also, detection of pulmonary nodules is a main method for early detection of lung cancer that can even heavily improve survival rate of lung cancer patients by reduced mortality rate to better level. Moreover, correct precision and incorrect detection of positive cancerous cell for lung nodules is both low and high. A lung cancer detection method using deep learning called, Residual Neural and Partial Derivative Multilayer Perceptron (RN-PDMP) is proposed. Initially the aid of lung cancer images acquired from IQ-OTH/NCCD Lung Cancer Dataset addresses class imbalance issue, therefore reducing the noise to a greater extent using Cross Entropy Focal Loss-based preprocessing model. Secondly preprocessed images as input, robust features are extracted by means of Census Transformed Residual Neural Network-based feature extraction algorithm. Finally, classification between normal, benign and malignant are made using Partial Derivative Multilayer Perceptron Deep Learning-based Classifier. The performance analysis of results describe to lung cancer detection model based on RN-PDMP integrating Forward Propagation-based Residual Learning has achieved a precision of 99% and recall of 97%. The results are comparatively better than existing methods in RN-PDMP method of lung cancer detection based on Partial Derivative-based back propagation is better recall for small samples and improves true positive pulmonary nodules.

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Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Cited by 21 publications

References 44 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

An Explainable Method for Lung Cancer Detection and Localisation from Tissue Images through Convolutional Neural Networks

Residual Partial Derivative Multilayer Perceptron Deep Learning Based Classifier for Lung Cancer Detection

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