An early prediction and classification of lung nodule diagnosis on CT images based on hybrid deep learning techniques.

Detection of malignant lung nodules at an early stage may allow for clinical interventions that increase the survival rate of lung cancer patients. Using hybrid deep learning techniques to detect nodules will improve the sensitivity of lung cancer screening and the interpretation speed of lung scans. Accurate detection of lung nodes is an important step in computed tomography (CT) imaging to detect lung cancer. However, it is very difficult to identify strong nodes due to the diversity of lung nodes and the complexity of the surrounding environment. Here, we proposed lung nodule detection and classification with CT images based on hybrid deep learning (LNDC-HDL) techniques. First, we introduce a chaotic bird swarm optimization (CBSO) algorithm for lung nodule segmentation using statistical information. Second, we illustrate an improved Fish Bee (IFB) algorithm for feature extraction and selection. Third, we develop a hybrid classifier i.e. hybrid differential evolution-based neural network (HDE-NN) for tumor prediction and classification. Experimental results have shown that the use of computed tomography, which demonstrates the efficiency and importance of the HDE-NN specific structure for detecting lung nodes on CT scans, increases sensitivity and reduces the number of false positives. The proposed method shows that the benefits of HDE-NN node detection can be reaped by combining clinical practice.

An automatic classification of pulmonary nodules for lung cancer diagnosis using novel LLXcepNN classifier.

INTRODUCTION: A critical step to ameliorate diagnosis and extend patient survival is Benign-malignant Pulmonary Nodule (PN) classification at earlier detection. On account of the noise of Computed Tomography (CT) images, the prevailing Lung Nodule (LN) detection techniques exhibit broad variation in accurate prediction. METHODS: Thus, a novel Nodule Detection along with Classification algorithm for early diagnosis of Lung Cancer (LC) has been proposed. Initially, employing the Adaptive Mode Ostu Binarization (AMOB) technique, the Lung Volumes (LVs) isextortedas of the image together with the extracted lung regions is pre-processed. Then, detection of LNs takes place, and utilizing Geodesic Fuzzy C-Means Clustering (GFCM) Segmentation Algorithm, it is segmented.Next, the vital features are extracted, and the Nodules are classified by utilizing Logarithmic Layer Xception Neural Network (LLXcepNN) Classifier grounded on the extracted feature. RESULTS: The nodules are classified as Benign Nodules (BN) and Malignant Nodules (MN) by the proposed classifier. Lastly, the Lung CT images are scrutinized. DISCUSSION: Thus, when weighed against the prevailing techniques, the proposed systems' acquired outcomes exhibit that the rate of accuracy of classification is enhanced.

1,5-Benzothiazepine Derivatives: Green Synthesis, In Silico and In Vitro Evaluation as Anticancer Agents.

Considering the importance of benzothiazepine pharmacophore, an attempt was carried out to synthesize novel 1,5-benzothiazepine derivatives using polyethylene glycol-400 (PEG-400)-mediated pathways. Initially, different chalcones were synthesized and then subjected to a cyclization step with benzothiazepine in the presence of bleaching clay and PEG-400. PEG-400-mediated synthesis resulted in a yield of more than 95% in less than an hour of reaction time. Synthesized compounds 2a-2j were investigated for their in vitro cytotoxic activity. Moreover, the same compounds were subjected to systematic in silico screening for the identification of target proteins such as human adenosine kinase, glycogen synthase kinase-3ß, and human mitogen-activated protein kinase 1. The compounds showed promising results in cytotoxicity assays; among the tested compounds, 2c showed the most potent cytotoxic activity in the liver cancer cell line Hep G-2, with an IC50 of 3.29 ± 0.15 µM, whereas the standard drug IC50 was 4.68 ± 0.17 µM. In the prostate cancer cell line DU-145, the compounds displayed IC50 ranges of 15.42 ± 0.16 to 41.34 ± 0.12 µM, while the standard drug had an IC50 of 21.96 ± 0.15 µM. In terms of structural insights, the halogenated phenyl substitution on the second position of benzothiazepine was found to significantly improve the biological activity. This characteristic feature is supported by the binding patterns on the selected target proteins in docking simulations. In this study, 1,5-benzothiazepines have been identified as potential anticancer agents which can be further exploited for the development of more potent derivatives.