A Transfer Learning Approach with a Convolutional Neural Network for the Classification of Lung Carcinoma

Abstract

Lung cancer is among the most hazardous types of cancer in humans. The correct diagnosis of pathogenic lung disease is critical for medication. Traditionally, determining the pathological form of lung cancer involves an expensive and time-consuming process investigation. Lung cancer is a leading cause of mortality worldwide, with lung tissue nodules being the most prevalent way for doctors to identify it. The proposed model is based on robust deep-learning-based lung cancer detection and recognition. This study uses a deep neural network as an extraction of features approach in a computer-aided diagnosing (CAD) system to assist in detecting lung illnesses at high definition. The proposed model is categorized into three phases: first, data augmentation is performed, classification is then performed using the pretrained CNN model, and lastly, localization is completed. The amount of obtained data in medical image assessment is occasionally inadequate to train the learning network. We train the classifier using a technique known as transfer learning (TL) to solve the issue introduced into the process. The proposed methodology offers a non-invasive diagnostic tool for use in the clinical assessment that is effective. The proposed model has a lower number of parameters that are much smaller compared to the state-of-the-art models. We also examined the desired dataset’s robustness depending on its size. The standard performance metrics are used to assess the effectiveness of the proposed architecture. In this dataset, all TL techniques perform well, and VGG 16, VGG 19, and Xception for 20 epoch structure are compared. Preprocessing functions as a wonderful bridge to build a dependable model and eventually helps to forecast future scenarios by including the interface at a faster phase for any model. At the 20th epoch, the accuracy of VGG 16, VGG 19, and Xception is 98.83 percent, 98.05 percent, and 97.4 percent.

Triple SVM Integrated with Enhanced Random Region Segmentation for Classification of Lung Tumors

“…Table III shows the comparative analysis of various methods. RRS with Triple SVM has been compared to previous researches such as, TL-VGG 16, TL-VGG 19 and TL-Xception [16], and SVM [14]. This comparison is given in Table III.…”

“…Result Obtained Particle Swarm Optimization, Genetic Algorithm, SVM Accuracy : 89.50% K-NN Classification using Genetic Algorithm Accuracy : 90% The comparative analysis of RRS with Triple SVM with existing researches such as TL-VGG 16 (Humayun et al [16]), TL-VGG 19, TL-Xception and SVM (Kareem et al [14]) are shown in the Table III. From the Table III, it is concluded that the RRS with Triple SVM achieves better results than the TL-VGG 16 (Humayun et al 2022), TL-VGG 19, TL-Xception and SVM. But, the proposed RRS is used to perform precise segmentation of lung tumor portions which leads to improve the classification using triple SVM.…”

“…According to the aforementioned research work, CNN performance might be enhanced in many cases to improve accuracy performance and also aids in the detection process of early diagnosis of the lung cancer disease with minimal error [15]. Humayun et al [16] presented the transfer learning (TL) with Convolutional Neural Network (CNN) to classify the lung disease. This work comprised three stages: Initially data augmentation was accomplished followed by pretrained CNN which was used to ensure the classification.…”

“…The following are some of the issues with the most recent ways to classifying lung diseases: In classification, it is necessary for testing accuracy to exceed training accuracy [16]. Because training uses the same input data for analysis while testing uses a different input data for categorization.…”

Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning

“…DL models such as convolutional neural networks (CNNs) have proven themselves superior to more traditional methods in various fields, especially image and feature recognition [ 28 ]. Moreover, they have been effectively applied in the medical profession, with phenomenal results and outstanding performance in a variety of challenging situations.…”