Segementation and Prediction from CT Images for Detecting Lung Cancer

Chethan, K S; Vishwanath, S.; Patil, Rakshith V; Vijetha, K A

doi:10.1109/icccnt49239.2020.9225486

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…A handful of work was done involving the use of pretrained models for the task of identifying lung diseases from the x-ray or CT images. [19][20][21][22][23] A recommendation of a super-resolution framework was put forward for enhanced nodule detection. Wu et al 24 gave a deep residual network framework in which ResNet50 was taken as a base model for classification on the LIDC-IDRI database.…”

Section: Literature Reviewmentioning

confidence: 99%

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Domain adaptation and weight initialization of neural networks for diagnosing interstitial lung diseases

Thorat

Salvi

Dedhia

et al. 2022

Int J Imaging Syst Tech

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Interstitial lung diseases (ILDs) are adverse disorders, damaging the lung tissues, thus making timely diagnosis imperative. To counter the scarcity of publicly available high‐resolution computed tomography (HRCT) data, architecture employing HRCT and x‐ray data have been proposed for diagnosing ILDs. A model is first trained to diagnose three ILDs and a healthy lung from x‐ray data and then with a small amount of HRCT data of the same four classes. We introduce an EfficientNet+AlexNet model and a custom deep convolutional neural network and compare their performances with different weight initialization at the start of x‐ray image training. The EfficientNet+AlexNet model initialized with ImageNet weights performed best on the x‐ray dataset and was later used for domain adaptation. We also compare our model's performance to a single pre‐trained EfficientNetB0 model (trained on the same HRCT data). Our model gave an accuracy of 97.4% on the testing dataset, a notable increase in the performance compared to the recently proposed methodologies. The experimental results and graphical plots depict the superiority of domain adaptation and the potentials of weights that are a starting point for training the models.

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Section: Literature Reviewmentioning

confidence: 99%

“…Correlation learning mechanism (CLM) has been explored for brain tumor detection from CT images in 18 due to faster learning and parallel implementation. A handful of work was done involving the use of pre‐trained models for the task of identifying lung diseases from the x‐ray or CT images 19–23 …”

Section: Literature Reviewmentioning

confidence: 99%

Domain adaptation and weight initialization of neural networks for diagnosing interstitial lung diseases

Thorat

Salvi

Dedhia

et al. 2022

Int J Imaging Syst Tech

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“…Persons. Regardless of gender, they get lung cancer, and this cancer is the deadliest disease in the world [3]. Smoking addiction, carcinogenic environments such as radioactive gasses, and air pollution are the leading causes of lung cancer; in addition to these causes, genetic factors also play a role in causing lung cancer [1].…”

Section: Introductionmentioning

confidence: 99%

“…𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒+𝐹𝑎𝑙𝑠𝑒 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒(3) Sensitivity: The sensitivity criterion means measuring the ratio of natural and positive cases that are predicted as positive cases by classification.The first stage: Pre-processingIn the previous sections, the explanations and the method of doing the work are presented in the preprocessing stage. The purpose of pre-processing the data is the area extraction related to the Lung from CT scan images.…”

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confidence: 99%

Intelligent Diagnosis of Lung Cancer Using Deep Learning

Bapir,

Alavi,

Naderan Tahan

2024

Front Health Inform

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Introduction: Lung cancer, a highly prevalent disease worldwide, poses a significant risk to individuals. Nodules, which manifest as minuscule masses in the lungs, serve as crucial indicators of the early stages of the disease, with the possibility of being either benign or malignant in nature. Prompt diagnosis of this ailment plays a pivotal role in saving patients' lives, thus rendering the utilization of computed aided diagnosis methods exceedingly valuable within this domain.Material and Methods: The methodology employed for presentation purposes is deeply rooted in the principles of deep learning, a field that epitomizes the amalgamation of artificial intelligence and neuroscience. Delving into the specifics, the initial phase of this process entails the preprocessing of data, wherein the lung area is meticulously isolated from computed tomography (CT) scan images. Subsequently, in the second stage, the identification of nodules is facilitated through the employment of the mask region convolutional neural network (RCNN) technique, which effectively entails the delineation of masks and bounding boxes. The third and final step involves the classification of the identified nodules, achieved through the utilization of a singular convolutional neural network, ultimately segregating the nodules into three distinct categories: benign, malignant, and ambiguous. In order to evaluate the efficacy of the proposed method, the LIDC-IDRI dataset was employed as a means of testing, thereby furnishing tangible evidence to substantiate the claim that the presented method is on par with its counterparts within the realm of detecting and classifying lung nodules.Results: It is worth noting that the proposed method has yielded a remarkable accuracy rate of 95% in the phase of nodule detection, further bolstering its credibility and reliability. Furthermore, the accuracy rate achieved during the step of nodule classification stands at an impressive 97.3%, thereby cementing the efficacy of the proposed method in a comprehensive manner.Conclusion: The purpose of this work to provide an intelligent system for reducing the amount of the workload of the physicians in this field. After examining and studying some data set, the LIDC-IDRI data set is presented, for this work because of being suitable for two works of detecting nodule place and nodule classification, lots of data, and because of being reliable and used in previous reliable works that can provide the ability to compare the results.

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A systematic review on feature extraction methods and deep learning models for detection of cancerous lung nodules at an early stage –the recent trends and challenges

Palani,

Rajagopal,

Chintanpalli

2024

Biomed. Phys. Eng. Express

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Lung cancer is one of the most common life-threatening worldwide cancers affecting both the male and female populations. The appearance of nodules in the scan image is an early indication of the development of cancer cells in the lung. The Low Dose Computed Tomography screening technique is used for the early detection of cancer nodules. Therefore, with more Computed Tomography (CT) lung profiles, an automated lung nodule analysis system can be utilized through image processing techniques and neural network algorithms. A CT image of the lung consists of many elements such as blood vessels, ribs, nodules, sternum, bronchi, and nodules. These nodules can be both benign and malignant, where the latter leads to lung cancer. Detecting them at an earlier stage can increase life expectancy by up to 5 to 10 years. To analyze only the nodules from the profile, respected features are calculated using image processing techniques. Based on the review, textural features were promising ones in medical image analysis and for solving computer vision problems. The significance of extracting the hidden features (textural) enables the higher performance of deep learning algorithms (DL) predominantly in medical imaging, which has resulted in improvement in accuracy in recent years. Earlier detection of cancerous lung nodules is possible through the combination of multi-featured extraction and classification techniques using image data. In this paper, we discussed the overview of lung cancer along with publicly available datasets for research purposes. The primary objective of the paper is to provide the importance of textural features when combined with different deep-learning models. It gives insights into their advantages, disadvantages, and limitations regarding possible research gaps. The paper compared the recent studies of deep learning models with and without feature extraction and concluded that DL models that include feature extraction were better than the others.

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Segementation and Prediction from CT Images for Detecting Lung Cancer

Cited by 5 publications

References 9 publications

Domain adaptation and weight initialization of neural networks for diagnosing interstitial lung diseases

Domain adaptation and weight initialization of neural networks for diagnosing interstitial lung diseases

Intelligent Diagnosis of Lung Cancer Using Deep Learning

A systematic review on feature extraction methods and deep learning models for detection of cancerous lung nodules at an early stage –the recent trends and challenges

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