This document discusses classifying breast cancer histopathology images using a convolutional neural network. It provides background on breast cancer and deep learning. It then describes using a ResNet50 model with convolutional and pooling layers for the image classification. The model was trained on batches of resized images over 20 epochs, and accuracy, loss, predicted vs actual results, a confusion matrix and ROC curve are presented to analyze the model's performance.

1. BREAST CANCER
CALSSIFICATION

2. BREAST CANCER & CLASSIFICATION:
 A cancer that forms in the cells of the breasts.
 Breast cancer can occur in women and rarely in men.
 Symptoms of breast cancer include a lump in the breast, bloody discharge
from the nipple and changes in the shape or texture of the nipple or breast
 In this study, various histopathological images of breast are taken.

3. WHAT IS DEEP LEARNING?
 Deep learning is a type of machine learning and artificial intelligence that imitates
the way humans gain certain types of knowledge.
 At its simplest, deep learning can be thought of as a way to automate predictive
analytics. While traditional machine learning algorithms are linear, deep learning
algorithms are stacked in a hierarchy.
 The idea of deep learning is achieved through multiple layers of neural networks.
Fig. 1

4. TYPES OF NEURAL NETWORKS:
Three types of neural networks:
 Artificial Neural Networks
 Convolutional Neural Networks
 Recurrent Neural Networks
Since we worked on images, for Image Classifications CNNs are used.
CNN architecture:

5. CONSTITUENTS OF CNN:
 Pooling layers are used to reduce the dimensions of the feature maps.
e.g. Max, Min and Avg. Pooling.
 Flattening is used to convert all the resultant 2-Dimensional arrays from pooled
feature maps into a single long continuous linear vector.
 The dense layer receives input from all the previous layer’s neurons. Its also
called the fully connected layer.
 The accuracy and the effectiveness of the model depends upon the number of
convolutional layers it has, Kernel size, Image resolution and Quality of image.
Here, we chose the ResNet50 Convolutional Model for the classification.
WHY RESNET-50?
 ResNet-50 is a convolutional neural network that is 50 layers deep.
 ResNet-50 is mainly used to overcome the ‘vanishing gradient problem’ i.e.
during backpropagation the value gradient decreases significantly , thus hardly
any weight change is made.

6. MODEL TRAINING:
 Here, we took the batch size as 16, resized the images to 224 X 224 X 3 for
faster computation. The number of epochs are set to 20.

7. ACCURACY AND LOSS PLOTS:
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8. PREDICTED RESULT VS ACTUAL RESULT:

9. CONFUSION MATRIX AND ROC PLOT:
 Confusion Matrix:
 Roc Curve:

10. THANK YOU
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