Intelligent classification of lung malignancies using deep learning techniques

Abstract

PurposeComputed tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. To detect the location of the cancerous lung nodules, this work uses novel deep learning methods. The majority of the early investigations used CT, magnetic resonance and mammography imaging. Using appropriate procedures, the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer. All of the methods used to discover and detect cancer illnesses are time-consuming, expensive and stressful for the patients. To address all of these issues, appropriate deep learning approaches for analyzing these medical images, which included CT scan images, were utilized.Design/methodology/approachRadiologists currently employ chest CT scans to detect lung cancer at an early stage. In certain situations, radiologists' perception plays a critical role in identifying lung melanoma which is incorrectly detected. Deep learning is a new, capable and influential approach for predicting medical images. In this paper, the authors employed deep transfer learning algorithms for intelligent classification of lung nodules. Convolutional neural networks (VGG16, VGG19, MobileNet and DenseNet169) are used to constrain the input and output layers of a chest CT scan image dataset.FindingsThe collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer, squamous and adenocarcinoma impacted chest CT scan images. According to the confusion matrix results, the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy, followed by VGG19 with 89.39%, MobileNet with 85.60% and DenseNet169 with 83.71% accuracy, which is analyzed using Google Collaborator.Originality/valueThe proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19, MobileNet and DenseNet169. The results are validated by computing the confusion matrix for each network type.

Multiround Transfer Learning and Modified Generative Adversarial Network for Lung Cancer Detection

“…(i) Source domain and target domain: the related works [9][10][11][12] formulated the transfer learning problem using a similar source domain and target domain whereas other works [13][14][15][16] considered the distant source and target domains. Our work considered 10 benchmark datasets to evaluate the MTL using similar and distant sources and target domains.…”

“…(viii) Accuracy: all related works and our work report the accuracy. Related works [13][14][15][16] only reported the results after applying the transfer learning model. Te percentage improvement of the accuracy is 7.80% [9], 3.77% [10], 1.34% [11], 5.88% [12], and 6.85-9.92% (our work).…”

“…Results revealed that ResNet50 with support vector machine achieved the best performance with sensitivity and accuracy of 85.4% and 88.4%, respectively. Te work also demonstrated the efectiveness of the pretrained model using ImageNet to perform transfer learning on the target domain of chest CT [14]. Four common architectures, namely, DenseNet169, MobileNet, VGG19, and VGG16 were used to build the LCD model.…”

“…Related Works. Although existing works [9][10][11][12][13][14][15][16] formulated the transfer learning problems with a single source domain and single target domain, the discussion has merit as these works fell into the same research area, i.e., transfer learning for LCD. In the following, two common types of formulations will be discussed: (i) transfer learning between the similar source domain and target domain [9][10][11][12] and (ii) transfer learning between the distant source domain and target domain [13][14][15][16].…”

In-situ quality inspection system of injection parts based on transfer learning

“…However, precisely delineating the boundary between the general and task-specific features is challenging. Various research endeavors substantiated the viability and applicability of TL across diverse domains, including medical (Albayrak, 2022; Wang et al , 2021; Yadlapalli et al , 2022), plant science (Joshi et al , 2021), mechanic (Mao et al , 2020) and additive manufacturing (Li et al , 2021). Nagorny et al (2020) used polarimetric images to train VGG16 and MobileNetV2 networks via TL approach for quality inspection of injection parts.…”