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According to the WHO (World Health Organization), lung cancer is the leading cause of cancer deaths globally. In the future, more than 2.2 million people will be diagnosed with lung cancer worldwide, making up 11.4% of every primary cause of cancer. Furthermore, lung cancer is expected to be the biggest driver of cancer-related mortality worldwide in 2020, with an estimated 1.8 million fatalities. Statistics on lung cancer rates are not uniform among geographic areas, demographic subgroups, or age groups. The chance of an effective treatment outcome and the likelihood of patient survival can be greatly improved with the early identification of lung cancer. Lung cancer identification in medical pictures like CT scans and MRIs is an area where deep learning (DL) algorithms have shown a lot of potential. This study uses the Hybridized Faster R-CNN (HFRCNN) to identify lung cancer at an early stage. Among the numerous uses for which faster R-CNN has been put to good use is identifying critical entities in medical imagery, such as MRIs and CT scans. Many research investigations in recent years have examined the use of various techniques to detect lung nodules (possible indicators of lung cancer) in scanned images, which may help in the early identification of lung cancer. One such model is HFRCNN, a two-stage, region-based entity detector. It begins by generating a collection of proposed regions, which are subsequently classified and refined with the aid of a convolutional neural network (CNN). A distinct dataset is used in the model's training process, producing valuable outcomes. More than a 97% detection accuracy was achieved with the suggested model, making it far more accurate than several previously announced methods. [ABSTRACT FROM AUTHOR]

Camellia sinensis (green tea leaves) which acts as a reducing agent was used for the reduction of graphene oxide (GO) to obtain reduced graphene oxide (RGO). Anionic surfactant SDS was used to enhance the stability of synthesized reduced graphene oxide nanoparticles. Characterized reduced graphene oxide nanoparticle grain size was calculated to be 3.92 nm from the X-ray diffraction method, whereas zeta potential was measured − 35.23 ± 5.45 mV at room temperature. Antioxidant and cell cytotoxicity against A-549 lung carcinoma cells were also studied. Phytochemical content of Camellia sinensis imparts feasible DPPH activity of 85.98 ± 2.49% against RGO, whereas ABTS scavenging activity was found to be 88.87 ± 1.74% followed by measurement of the total phenolic content of 842 ± 13.33 µg/gm. RGO at concentration 400 µg/ml showed an optimum level of hemolysis at pH 7.4 (4.92 ± 1.20%) than pH 5.6 (11.15 ± 0.03%). Cytotoxicity activity studied by MTT assay of RGO on A-549 lung carcinomas cells was compared with drug doxorubicin. The bandgap energy of RGO was calculated to be 3.97 eV from absorption data, hence reveals the generation of oxidative stress in the A-549 lung cancer cell line. Thus, the surfactant and phytochemicals found in Camellia sinensis enhanced the stability of RGO, thereby providing enough energy to destabilize the target cells without affecting healthy cells, hence suggests its role in therapeutics application. [ABSTRACT FROM AUTHOR]