A Neural Network and Optimization Based Lung Cancer Detection System in CT Images

Venkatesh, C.; Ramana, Kadiyala; Lakkisetty, Siva Yamini; Band, Shahab S.; Agarwal, Shweta; Mosavi, Amir

doi:10.3389/fpubh.2022.769692

Cited by 20 publications

(10 citation statements)

References 32 publications

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“…Figure 6 depicts the DICE value scored on the considered dataset. The loss function of Mask R-CNN is an aggregation of three losses: grouping loss, bounding rectangle regression loss and mask loss as given in (1). The classification loss and boundary rectangle regression loss are the same as in Faster R-CNN.…”

Section: Resultsmentioning

confidence: 99%

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Advanced mask region-based convolutional neural network based deep-learning model for lung cancer detection

Krishna,

Madigondanahalli Thimmaiah

2024

IJ-AI

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Millions of individuals are affected each year by lung cancer, a serious global health concern. It may also cause numerous potentially fatal pulmonary problems, including infections, hemorrhage, or collapse. Finding a consistent and an effective way to ascertain lung cancer using medical imaging techniques is one of the primary issues in medical image processing. The difficulty of this task stems from the fact that the regions of the lungs that are affected by cancer might differ greatly in expressions of their size, location, shape, and aesthetics. Identifying whether the identified area is benign (non-cancerous) or malignant (cancerous) is another difficult task. Finding the appropriate course of treatment for the patient will depend on this. A critical stage in the identification of lung malignancy is identifying the knobs that are expected to be malevolent. To solve these issues, in this study work we employ a deep learning methodology based on Mask region-based convolutional neural network (Mask-RCNN). For the purpose of identifying and locating infected lung regions on computed tomography (CT) scan images, model is built utilizing the customized Mask-RCNN. In accordance with the evaluation's findings, the model scored 99.32% for accuracy and 99.45% for mean DICE, respectively.

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Section: Resultsmentioning

confidence: 99%

“…For the purpose of identifying lung cancer nodules from CT scans, Venkatesh et al [1] offer a unique method that combines Otsu thresholding and the cuckoo search algorithm. The identified nodules are then classified as either benevolent or malicious using a CNN.…”

Section: Introductionmentioning

confidence: 99%

Advanced mask region-based convolutional neural network based deep-learning model for lung cancer detection

Krishna,

Madigondanahalli Thimmaiah

2024

IJ-AI

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“…In [8], an efficient screening mechanism was designed to not only identifying lung cancer lesions but also improve the accuracy rate in swift manner. In this aspect, a segmentation mechanism using Otsu threshold was deployed with the purpose of isolating the nodules area.…”

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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“…ECG pulses are converted into 2D images through limited-time Fourier transform to authenticate normal ECGs and forecast premature cardiac demise from heart damage, such as arrhythmias or severe cardiac inability. The exactness of various timeframes was examined [1,[25][26][27]. With 4 min ECG, we have diagnosed heart failure spontaneously at 100 percent, arrhythmia at 97.9 percent, and abrupt cardiac arrest at 100 percent [28][29][30][31].…”

Section: Literature Surveymentioning

confidence: 99%

An Efficient Multilevel Thresholding Scheme for Heart Image Segmentation Using a Hybrid Generalized Adversarial Network

Reddy

Kola

Jayaram³

et al. 2022

Journal of Sensors

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Most people worldwide, irrespective of their age, are suffering from massive cardiac arrest. To detect heart attacks early, many researchers worked on the clinical datasets collected from different open-source datasets like PubMed and UCI repository. However, most of these datasets have collected nearly 13 to 147 raw attributes in textual format and implemented traditional data mining approaches. Traditional machine learning approaches just analyze the data extracted from the images, but the extraction mechanism is inefficient and it requires more number of resources. The authors of this research article proposed a system that is aimed at predicting heart attacks by integrating the techniques of computer vision and deep learning approaches on the heart images collected from the clinical labs, which are publicly available in the KAGGLE repository. The authors collected live images of the heart by scanning the images through IoT sensors. The primary focus is to enhance the quality and quantity of the heart images by passing through two popular components of GAN. GAN introduces noise in the images and tries to replicate the real-time scenarios. Subsequently, the available and newly created images are segmented by applying a multilevel threshold operation to find the region of interest. This step helps the system to predict the accurate attack rate by considering various factors. Earlier researchers have obtained sound accuracy by generating similar heart images and found the ROI parts of the 2D echo images. The proposed methodology has achieved an accuracy of 97.33% and a 90.97% true-positive rate. The reason for selecting the computed tomography (CT-SCAN) images is due to the gray scale images giving more reliable information at a low computational cost.

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A Neural Network and Optimization Based Lung Cancer Detection System in CT Images

Cited by 20 publications

References 32 publications

Advanced mask region-based convolutional neural network based deep-learning model for lung cancer detection

Advanced mask region-based convolutional neural network based deep-learning model for lung cancer detection

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

An Efficient Multilevel Thresholding Scheme for Heart Image Segmentation Using a Hybrid Generalized Adversarial Network

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