CerviLearnNet: Advancing cervical cancer diagnosis with reinforcement learning-enhanced convolutional networks.

Women tend to face many problems throughout their lives; cervical cancer is one of the most dangerous diseases that they can face, and it has many negative consequences. Regular screening and treatment of precancerous lesions play a vital role in the fight against cervical cancer. It is becoming increasingly common in medical practice to predict the early stages of serious illnesses, such as heart attacks, kidney failure, and cancer, using machine learning-based techniques. To overcome these obstacles, we propose the use of auxiliary modules and a special residual block, to record contextual interactions between object classes and to support the object reference strategy. Unlike the latest state-of-the-art classification method, we create a new architecture called the Reinforcement Learning Cancer Network, "RL-CancerNet", which diagnoses cervical cancer with incredible accuracy. We trained and tested our method on two well-known publicly available datasets, SipaKMeD and Herlev, to assess it and enable comparisons with earlier methods. Cervical cancer images were labeled in this dataset; therefore, they had to be marked manually. Our study shows that, compared to previous approaches for the assignment of classifying cervical cancer as an early cellular change, the proposed approach generates a more reliable and stable image derived from images of datasets of vastly different sizes, indicating that it will be effective for other datasets.

Enhanced biodegradation of hydrocarbons in soil by microbial biosurfactant, sophorolipid.

Effectiveness of a microbial biosurfactant, sophorolipid, was evaluated in washing and biodegradation of model hydrocarbons and crude oil in soil. Thirty percent of 2-methylnaphthalene was effectively washed and solubilized with 10 g/L of sophorolipid with similar or higher efficiency than that of commercial surfactants. Addition of sophorolipid in soil increased biodegradation of model compounds: 2-methylnaphthalene (95% degradation in 2 days), hexadecane (97%, 6 days), and pristane (85%, 6 days). Also, effective biodegradation method of crude oil in soil was observed by the addition of sophorolipid, resulting in 80% biodegradation of saturates and 72% aromatics in 8 weeks. These results showed the potentials of the microbial biosurfactant, sophorolipid, as an effective surfactant for soil washing and as an in situ biodegradation enhancer.