Breast Cancer Classification

Breast cancer is the world's second most frequent malignancy in both women and men. Because the true cause of breast cancer is difficult to pinpoint, early identification is critical for lowering the death rate from breast cancer-related complications. It accounted for around 12% of all new cancer cases in 2012, and 25% of all cancers in women. Early identification of cancer increases the chances of survival by up to 8%.

Breast cancer arises when cells in the breast begin to grow out of control. Breast images from mammograms, X-rays, and MRIs are mostly evaluated by radiologists to detect abnormalities. These cells normally grow into a tumor, which can be visible on x-rays or felt like a bump. Even experienced radiologists, however, have difficulty detecting characteristics such as microcalcifications, lumps, and masses, resulting in a high rate of false positives and false negatives. If the cells in the tumor can develop into (invade) surrounding tissues or spread (metastasize) to other parts of the body, it is considered malignant (cancer). Recent advances in image processing and deep learning raise the possibility of developing more advanced applications for breast cancer early detection. So, have a look at this site to learn about data science projects for final year students as well as data science projects for beginners.

Introduction

Breast cancer is a type of cancer that develops in the cells of the breast and is a fairly prevalent cancer in women. This is one of the data science projects ideas for data scientists. Breast cancer is the world's second most frequent malignancy, affecting both men and women. Breast cancer, like lung cancer, is a life-threatening condition for women. In 2012, it accounted for around 12% of all new cancer cases and 25% of all cancers in women. Breast cancer is classified into different categories based on how the cells appear under a microscope. Invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS) are the two most common kinds of breast cancer, with the latter progressing slowly and having little impact on patients' daily life. These cells normally form a tumor, which can be seen on an x-ray or felt as a lump. The DCIS type accounts for a small fraction of all occurrences (between 20% and 53%); on the other hand, the IDC form is more harmful, encircling the entire breast tissue. The tumor is malignant (cancer) if the cells can develop into (invade) surrounding tissues or spread to other parts of the body (metastasize). Approximately 80% of breast cancer patients fall into this category.

Breast cancer can be efficiently treated if detected early. Laboratory technicians prepare histopathological slides from patients' breast cancer tissues by first staining the cell nuclei blue with hematoxylin, then counter-staining the cytoplasmic and non-nuclear components with eosin in various shades to highlight the different parts of transparent tissue structures and cellular features. As a result, having access to appropriate screening tools is critical for detecting the first signs of breast cancer.  It is the great data science project idea to work on.

The microscopic examination of stained biopsy tissues of breast cancer is then used to create digital histopathology images. Various imaging modalities are used to screen for this condition, with mammography, ultrasonography, and thermography being the most common. Despite the fact that these images give pathologists (humans) a comprehensive perspective, errors can still occur if the diagnosis becomes too time-consuming due to the large-sized slides. Mammography is one of the most important early detection procedures for breast cancer.

To address this issue, many researchers are focused on employing deep learning algorithms to analyze histopathology pictures in order to enhance cancer diagnosis accuracy. Because mammography is ineffective for firm breasts, ultrasound or diagnostic sonography procedures are commonly employed. The goal of this study was to demonstrate deep learning approaches in the field of histopathology image categorization in breast cancer. Small masses can be skipped by radiations from radiography, and thermography may be more successful than ultrasonography in identifying tiny malignant masses, given these limitations.  Let's get on with this data science project step by step.

Loading all the libraries and dependencies.

 

import json

import math

import os

import cv2

from PIL import Image

import numpy as np

from keras import layers

from keras.applications import DenseNet201

from keras.callbacks import Callback, ModelCheckpoint, ReduceLROnPlateau, TensorBoard

from keras.preprocessing.image import ImageDataGenerator

from keras.utils.np_utils import to_categorical

from keras.models import Sequential

from keras.optimizers import Adam

import matplotlib.pyplot as plt

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.metrics import cohen_kappa_score, accuracy_score

import scipy

from tqdm import tqdm

import tensorflow as tf

from keras import backend as K

import gc

from functools import partial

from sklearn import metrics

from collections import Counter

import json

import itertools

Final Thoughts

Early detection of breast cancer can assist to reduce the death rate associated with the disease. Although this data science project is far from finished, the success of deep learning in such a wide range of real-world issues is astonishing. Deep learning and image processing's extraordinary performance has sparked the creation of automated medical applications. It's incredible to see deep learning come to fruition in such a wide range of real-world situations. By measuring the learning rate, an upgraded CNN was created to further improve the model.  In this blog, I showed how

to use convolutional neural networks with transfer learning to classify benign and malignant breast cancer from a set of microscopic photos. The model was found to be overfitting during the initial testing phase, thus the parameters were improved. Using Deep Learning and Python for coding, this post demonstrated how to differentiate benign and malignant breast cancer from a collection of small photos. In addition, approaches for detecting and classifying abnormalities in the breast at an early stage, as well as choosing the most effective treatment for the patient, should be improved in order to reduce breast cancer-related mortality.  If you want to know more about data science projects step by step then check out our website Learnbay: data science course in Chennai for more information. 

 

Keywords	Search Volume	Competition
data science project	17,480	25 (Low)
data science projects ideas	7,690	30 (Low)
data science project for final year	1,180	30(Low)
Data science project for beginners	7,500	26(Low)
data science projects step by step	2,240	31 (Low)