Attention Based Recurrent Neural Network for Lung Cancer Detection

Sasikumar, S.; Renjith, P. N.; Ramesh, K.; Sankaran, Kris

doi:10.1109/i-smac49090.2020.9243556

Cited by 10 publications

(5 citation statements)

References 6 publications

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“…The following are the paper’s significant contributions [ 8 ]: Improved CNN Model: The CNN’s efficiency is optimized to boost the lung cancer diagnostic performance, and its computation is based on various inputs. The CNN calculates the improved range of values based on the extracted features [ 9 ]. Hyperparameter Tuning: The learning parameters for the CNN must be generated on demand.…”

Section: Introductionmentioning

confidence: 99%

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Optimization System Based on Convolutional Neural Network and Internet of Medical Things for Early Diagnosis of Lung Cancer

Ali

Chooralil²,

Balasubramanian

et al. 2023

Bioengineering

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Recently, deep learning and the Internet of Things (IoT) have been widely used in the healthcare monitoring system for decision making. Disease prediction is one of the emerging applications in current practices. In the method described in this paper, lung cancer prediction is implemented using deep learning and IoT, which is a challenging task in computer-aided diagnosis (CAD). Because lung cancer is a dangerous medical disease that must be identified at a higher detection rate, disease-related information is obtained from IoT medical devices and transmitted to the server. The medical data are then processed and classified into two categories, benign and malignant, using a multi-layer CNN (ML-CNN) model. In addition, a particle swarm optimization method is used to improve the learning ability (loss and accuracy). This step uses medical data (CT scan and sensor information) based on the Internet of Medical Things (IoMT). For this purpose, sensor information and image information from IoMT devices and sensors are gathered, and then classification actions are taken. The performance of the proposed technique is compared with well-known existing methods, such as the Support Vector Machine (SVM), probabilistic neural network (PNN), and conventional CNN, in terms of accuracy, precision, sensitivity, specificity, F-score, and computation time. For this purpose, two lung datasets were tested to evaluate the performance: Lung Image Database Consortium (LIDC) and Linear Imaging and Self-Scanning Sensor (LISS) datasets. Compared to alternative methods, the trial outcomes showed that the suggested technique has the potential to help the radiologist make an accurate and efficient early lung cancer diagnosis. The performance of the proposed ML-CNN was analyzed using Python, where the accuracy (2.5–10.5%) was high when compared to the number of instances, precision (2.3–9.5%) was high when compared to the number of instances, sensitivity (2.4–12.5%) was high when compared to several instances, the F-score (2–30%) was high when compared to the number of cases, the error rate (0.7–11.5%) was low compared to the number of cases, and the computation time (170 ms to 400 ms) was low compared to how many cases were computed for the proposed work, including previous known methods. The proposed ML-CNN architecture shows that this technique outperforms previous works.

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Section: Introductionmentioning

confidence: 99%

“…Improved CNN Model: The CNN’s efficiency is optimized to boost the lung cancer diagnostic performance, and its computation is based on various inputs. The CNN calculates the improved range of values based on the extracted features [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Optimization System Based on Convolutional Neural Network and Internet of Medical Things for Early Diagnosis of Lung Cancer

Ali

Chooralil²,

Balasubramanian

et al. 2023

Bioengineering

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“…A neural network was used to diagnose cancer using COPD and pneumonia. 8 Image recognition involves the identification and categorization of the objects, textures, and patterns in an image. In industries such as healthcare, marketing, transportation, and e-commerce, image recognition has a multitude of uses.…”

Section: Introductionmentioning

confidence: 99%

“…AEO is a population-based algorithm that is designed to find optimal solutions to complex optimization problems. A neural network was used to diagnose cancer using COPD and pneumonia 8 . Image recognition involves the identification and categorization of the objects, textures, and patterns in an image.…”

Section: Introductionmentioning

confidence: 99%

Deep learning modified neural networks with chicken swarm optimization-based lungs disease detection and severity classification

et al. 2023

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Many people around the world are affected by pulmonary disease as well as asthma, pneumonia, lung cancer, and tuberculosis. All of these conditions have one thing in common: airway obstruction. Lung illnesses are a worldwide issue in which chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and fibrosis are few of the most common. An accurate diagnosis of a lung problem is essential, and it has been pursued by many researchers using image processing and machine learning models. Multiple deep learning methods are used to predict lung disease; these include convolutional neural networks, vanilla neural networks, visual geometry group-based neural networks, and the capsule network. Medical data are scarce, so diagnosing pulmonary diseases using chest x-ray pictures from datasets with less than 1000 samples is considered to address the problem. Three deep learning multiple neural networks (DLMNNs) were generated using the chicken swarm optimization (CSO) approach, for which transfer learning was applied to evaluate the performance of each. First, we created an algorithm for segmenting CXR images, and then we compared it with other classification systems. Our results were compared with those of other methods using publicly available data from the Shenzhen and Montgomery lung datasets. However, our technique has a lower number of trainable parameters compared with the best-performing models trained on the Montgomery dataset. The DLMNN-CSO virtually matched the best performance on the Shenzhen dataset, although it was computationally less expensive than the other models. DLMNN-CSO's validation loss was 0.4, whereas the validation loss for CNN, VDSNet, VGG, and DBN was 0.9, 0.8, 0.6, and 0.5, respectively.

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“…There is a lot of work done using AI technology to detect and diagnose lung diseases [5,6]. As a kind of probabilistic neural network, multilayer perceptron, learning vector quantization [7], and recurrent neural network (RNN) have been used to diagnose lung disease [8]. For the detection of lung diseases such as tuberculosis Sahlol, Ahmed T et al [9] proposed a new hybrid method for tuberculosis X-ray image classification by extracting features from X-ray images with transfer learning and then filtering the produced huge number of features using the recently proposed Artificial Ecosystem-based Optimization (AEO); this method surpassed 90% accuracy in two datasets.…”

Section: Introductionmentioning

confidence: 99%

Classification and Predictions of Lung Diseases from Chest X-rays Using MobileNet V2

Souid

Sakli²,

Sakli

2021

Applied Sciences

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Featured Application: The method presented in this paper can be applied in medical computer systems for supporting medical diagnosis.Abstract: Thoracic radiography (chest X-ray) is an inexpensive but effective and widely used medical imaging procedure. However, a lack of qualified radiologists severely limits the applicability of the technique. Even current Deep Learning-based approaches often require strong supervision, e.g., annotated bounding boxes, to train such systems, which is impossible to harvest on a large scale. In this work, we proposed the classification and prediction of lung pathologies of frontal thoracic X-rays using a modified model MobileNet V2. We considered using transfer learning with metadata leverage. We used the NIH Chest-Xray-14 database, and we did a comparison of performance of our approach to other state-of-the-art methods for pathology classification. The main comparison was by Area under the Receiver Operating Characteristic Curve (AUC) statistics and analyzed the differences between classifiers. Overall, we notice a considerable spread in the achieved result with an average AUC of 0.811 and an accuracy above 90%. We conclude that resampling the dataset gives a huge improvement to the model performance. In this work, we intended to create a model that is capable of being trained, and modified devices with low computing power because they can be implemented into smaller IoT devices.

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Attention Based Recurrent Neural Network for Lung Cancer Detection

Cited by 10 publications

References 6 publications

Optimization System Based on Convolutional Neural Network and Internet of Medical Things for Early Diagnosis of Lung Cancer

Optimization System Based on Convolutional Neural Network and Internet of Medical Things for Early Diagnosis of Lung Cancer

Deep learning modified neural networks with chicken swarm optimization-based lungs disease detection and severity classification

Classification and Predictions of Lung Diseases from Chest X-rays Using MobileNet V2

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