The detection of lung cancer using massive artificial neural network based on soft tissue technique

Multi-classification approach for lung nodule detection and classification with proposed texture feature in X-ray images

Lung cancer is a widespread type of cancer around the world. It is, moreover, a lethal type of tumor. Nevertheless, analysis signifies that earlier recognition of lung cancer considerably develops the possibilities of survival. By deploying X-rays and Computed Tomography (CT) scans, radiologists could identify hazardous nodules at an earlier period. However, when more citizens adopt these diagnoses, the workload rises for radiologists. Computer Assisted Diagnosis (CAD)-based detection systems can identify these nodules automatically and could assist radiologists in reducing their workloads. However, they result in lower sensitivity and a higher count of false positives. The proposed work introduces a new approach for Lung Nodule (LN) detection. At first, Histogram Equalization (HE) is done during pre-processing. As the next step, improved Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) based segmentation is done. Then, the characteristics, including "Gray Level Run-Length Matrix (GLRM), Gray Level Co-Occurrence Matrix (GLCM), and the proposed Local Vector Pattern (LVP)," are retrieved. These features are then categorized utilizing an optimized Convolutional Neural Network (CNN) and itdetectsnodule or non-nodule images. Subsequently, Long Short-Term Memory (LSTM) is deployed to categorize nodule types (benign, malignant, or normal). The CNN weights are fine-tuned by the Chaotic Population-based Beetle Swarm Algorithm (CP-BSA). Finally, the superiority of the proposed approach is confirmed across various measures. The developed approach has exhibited a high precision value of 0.9575 for the best case scenario, and high sensitivity value of 0.9646 for the mean case scenario. The superiority of the proposed approach is confirmed across various measures.

A bi-directional deep learning architecture for lung nodule semantic segmentation

Lung nodules are abnormal growths and lesions may exist. Both lungs may have nodules. Most lung nodules are harmless (not cancerous/malignant). Pulmonary nodules are rare in lung cancer. X-rays and CT scans identify the lung nodules. Doctors may term the growth a lung spot, coin lesion, or shadow. It is necessary to obtain properly computed tomography (CT) scans of the lungs to get an accurate diagnosis and a good estimate of the severity of lung cancer. This study aims to design and evaluate a deep learning (DL) algorithm for identifying pulmonary nodules (PNs) using the LUNA-16 dataset and examine the prevalence of PNs using DB-Net. The paper states that a new, resource-efficient deep learning architecture is called for, and it has been given the name of DB-NET. When a physician orders a CT scan, they need to employ an accurate and efficient lung nodule segmentation method because they need to detect lung cancer at an early stage. However, segmentation of lung nodules is a difficult task because of the nodules' characteristics on the CT image as well as the nodules' concealed shape, visual quality, and context. The DB-NET model architecture is presented as a resource-efficient deep learning solution for handling the challenge at hand in this paper. Furthermore, it incorporates the Mish nonlinearity function and the mask class weights to improve segmentation effectiveness. In addition to the LUNA-16 dataset, which contained 1200 lung nodules collected during the LUNA-16 test, the LUNA-16 dataset was extensively used to train and assess the proposed model. The DB-NET architecture surpasses the existing U-NET model by a dice coefficient index of 88.89%, and it also achieves a similar level of accuracy to that of human experts.

Cancer recognition of artificial intelligence

The recognition mechanism of artificial intelligence (AI) is an interesting topic in understanding AI neural networks and their application in therapeutics. A number of multilayered neural networks can recognize cancer through deep learning. It would be interesting to think about whether human insights and AI attention are associated with each other or should be translated, which is one of the main points in this editorial. The automatic detection of cancer with computer-aided diagnosis is being applied in the clinic and should be improved with feature mapping in neural networks. The subtypes and stages of cancer, in terms of progression and metastasis, should be classified with AI for optimized therapeutics. The determination of training and test data during learning and selection of appropriate AI models will be essential for therapeutic applications.