Page 89 - sjsi

Research Article: Alsibai & Heydari
corresponding image dimension. The pixel
values of the image are also normalized to the
range [0,1] when converted to tensors.
3. Normalize the images: It is common to
normalize the images by subtracting the mean
and dividing by the standard deviation of the
image pixels. This helps to center the data and
improve the model's ability to learn from it. The
mean and standard deviation values for each
color channel are:
mean = [0.485, 0.456, 0.406] std = [0.229, 0.224,
0.225]
This is done due to it being mentioned as a
necessary pre-process in the documentation (16)
of DenseNet201.
4. Transforming all ultrasound images to
grayscale as this will eliminate the unnecessary
color channels if they exist and the resultant
image will have only 1 color channel due to it
being grayscale.
5. The images and their corresponding labels are
turned into batches where each batch consists of
32 images. This is very beneficial for a variety
of reasons:
 Memory constraints: Training a deep learning
model on a large dataset can require a lot of
memory. Grouping the images into batches
allows the model to train on a subset of the data
at a time, which can be more memory efficient.
 Computational efficiency: Processing the
images in batches can be more computationally
efficient, as it allows the model to make better
use of the parallel processing capabilities of
modern hardware such as GPUs.
 Stochastic gradient descent: When training a
model using stochastic gradient descent, it is
common to update the model's weights using the
gradients computed on a small batch of data
rather than the entire dataset. This is because
computing the gradients on the full dataset can
be computationally expensive, and using a
smaller batch of data can provide a good
approximation.
 Regularization: Using small batches of data
can also introduce noise into the learning
process, which can act as a form of
regularization and help the model generalize
better to new data.
SJSI – 2023: VOLUME 1-1
89
Model Description
After pre-processing the data, a classification
model is needed. Reviewing related work, most
of research done in the medical field that uses
deep learning utilizes transfer learning to train
on the data through a pre-trained model. In
fact, the conclusion of paper (17) mentions that
transfer learning is the best available option for
training even if the data model is pre-trained
on, it is weakly related to data in hand.
Thus, in order to train on this dataset, the
DenseNet architecture (18) was chosen.
DenseNet is one of the leading architectures
used in ultrasonography analysis when
conducting transfer learning as table 3 in this
paper (19) shows. DenseNet is a variation of the
traditional CNN architecture that uses dense
blocks, where each layer in a block is connected
to every other layer in the block. This allows the
network to pass information from earlier layers
to later layers more efficiently, alleviate the
problem of vanishing gradients in deeper
networks, and can improve performance on
tasks such as image classification. There are
several variations of the DenseNet architecture,
such as DenseNet121, DenseNet201, and
DenseNet264. Each variation refers to the
number of layers it consists of. DenseNet201 is
the variation chosen for this project as it has a
moderate number of layers and it is not too
computationally demanding. DenseNet and
many other pre-trained architectures are pre-
trained on the ImageNet dataset which is long-
standing landmark in computer vision (20) . It
consists of 1,281,167 training images, 50,000
validation images, and 100,000 test images
belonging to 1000 classes. These images consist
of a wide variety of scenes, covering a diverse
range of categories such as animals, vehicles,
household objects, food, clothing, musical
instruments, body parts, and much more. A
question might arise from this information, why
would a model trained on a such unrelated
dataset might be used for training on and
predicting medical images? In fact, the paper
titled “A scoping review of transfer learning