Impact of Adam, Adadelta, SGD on CNN for White Blood Cell Classification
Rahul Singh, Avinash Sharma, Neha Sharma, Rupesh Gupta
Abstract
The assessment of a patient’s blood sample is a critical obligation in the healthcare industry. Different health problems are caused by aberrant blood cell growth. One of the vital elements of blood are white blood cells (WBC). Manually identifying WBC under a microscope requires a significant amount of time and effort, and this procedure is susceptible to human error, which might result in wrong interpretation. The automated systems for recognizing and categorizing the various types of WBC have major medicinal applications in this field. Despite the fact that the deep convolution neural network method gave decent results for categorizing WBC images. In the proposed study, a CNN-based deep learning algorithm is used to identify WBC. The dataset contains a variety of white blood cells, including neutrophils, lymphocytes, eosinophils, basophils, and monocytes. The CNN model was tested using multiple optimizers with batch sizes of 32 and 10 epochs. For the CNN model study, performance metrics such as Precision, Recall, F1-Score, and Accuracy were employed. The suggested model’s greatest accuracy is 93%. The proposed methodology provides exact and speedy results, which may assist save patients’ lives.