An analysis of Heptoprotective effects of plant extract on albino rats by Deep Learning analysis using ResNet and VGG16 algorithms

Abstract

The hepatoprotective and anti-inflammatory effects of Terminalia catappa L. and Pergularia daemia hydroalcoholic extracts in albino rats with CCl4-induced liver damage (HAETC and HAEPD, respectively). The extracts significantly reduced the harm induced by CCl4 when given orally at a dosage of 200mg/kg. They also showed strong hepatoprotective and antioxidant properties. Wistar albino rats in good health were divided into seven groups, each with six animals. Group I acted as the control and received no treatment, whereas Group II was given 30% CCl4 (1ml/kg, intraperitoneally) to cause hepatotoxicity. After administering Silymarin (25 mg/kg, orally) as per protocol, Group III underwent CCl4 therapy. The CCl4-induced rats showed significant reductions in antioxidant enzymes like SOD, catalase, glutathione peroxidase (GPX), and glutathione-S-transferase (GST), as well as increases in serum marker enzymes like SGOT, SGPT, alkaline phosphatase (ALK), ACP, LDH, and lipid peroxidation (LPO) activity. However, rats given HAEPD and HAETC showed significant improvements, returning these parameters to levels that are almost normal (P 0.001).The hepatoprotective and antioxidant benefits of Terminalia catappa L. (HAETC) and Pergularia daemia (HAEPD) against CCl4-induced hepatotoxicity in rats were further validated by histopathological analysis of liver tissues. It encourages the use of pre-trained CNN architectures, such as VGG16 and ResNet (Residual Network), in the proposed method, which uses convolutional neural networks (CNNs) for histopathological image analysis to assess the hepatoprotective properties of Pergularia daemia and Terminalia catappa L. leaf extracts against CCl4-induced hepatotoxicity. These well-known CNN models may be modified for precise and effective histopathological image interpretation, assisting in the evaluation of the impact of plant extracts on liver health.