Accurate Prediction of Bacterial Infections using Deep Learning-Based Approaches: A Recent Study and Progress

Bacterial diseases cause a major threat to the health globally which necessitates to its accurate detection as well as diagnosis. There are various traditional methods like clinical assessments, laboratory techniques, microbiological tests etc which no doubt are effective but consumes a lot of time and relies completely on experienced professionals for validation. Hence, to address such challenges, deep learning models have been termed as game changers as they are capable of analysing the data rapidly, diagnosing the patient on time, and minimizing the human errors. This manuscript provides complete information related to the techniques which have been used for the detection and classification of bacterial diseases. It commences with a concise introduction to bacterial diseases and an exploration of conventional diagnostic methods. Subsequently, it delves into the transformative impact of deep learning models on disease detection and classification. The work of researchers in this domain is examined and compared by highlighting their limitations, which serve as the foundation for the proposed system. In essence, this manuscript offers a detailed examination of research papers that leverage such AI learning models for bacterial disease detection and classification, paving the way for more efficient and accurate healthcare practices.