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

Abstract

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.