  This paper investigates how adjustments to deep learning architectures impact
model performance in image classification. Small-scale experiments generate
initial insights although the trends observed are not consistent with the
entire dataset. Filtering operations in the image processing pipeline are
crucial, with image filtering before pre-processing yielding better results.
The choice and order of layers as well as filter placement significantly impact
model performance. This study provides valuable insights into optimizing deep
learning models, with potential avenues for future research including
collaborative platforms.
