Abstract:
In this work, we introduce the identification of the MNIST database, which will be in
handwritten digits that the machine can identify. The human handwriting form may be de-
tected and converted into computer language. We use several machine learning algorithms,
including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long
Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM).
The MNIST database was developed using binary images of handwritten numbers (09)
from NIST’s Special Database.Second, introduce the identification of TB photos. Tuber-
culosis is the biggest cause of mortality worldwide, according to the World Health Organi-
zation. Inadequate treatment and delayed or incorrect diagnosis have led to several cases
of the illness. Accurate and timely diagnosis is critical for successfully managing and
preventing tuberculosis. Despite significant progress in deep learning for medical image
processing. There are two types of distributions: training and application data.Our findings
show that transfer learning from a pre-trained vision transformer outperforms a pre-trained
CNN in medical imaging.
