Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN)

Abstract

The real cause of breast cancer is very challenging to determine and therefore early detection of the disease is necessary for reducing the death rate due to risks of breast cancer. Early detection of cancer boosts increasing the survival chance up to 8%. Primarily, breast images emanating from mammograms, X-Rays or MRI are analyzed by radiologists to detect abnormalities. However, even experienced radiologists face problems in identifying features like micro-calcifications, lumps and masses, leading to high false positive and high false negative. Recent advancement in image processing and deep learning create some hopes in devising more enhanced applications that can be used for the early detection of breast cancer. In this work, we have developed a Deep Convolutional Neural Network (CNN) to segment and classify the various types of breast abnormalities, such as calcifications, masses, asymmetry and carcinomas, unlike existing research work, which mainly classified the cancer into benign and malignant, leading to improved disease management. Firstly, a transfer learning was carried out on our dataset using the pre-trained model ResNet50. Along similar lines, we have developed an enhanced deep learning model, in which learning rate is considered as one of the most important attributes while training the neural network. The learning rate is set adaptively in our proposed model based on changes in error curves during the learning process involved. The proposed deep learning model has achieved a performance of 88% in the classification of these four types of breast cancer abnormalities such as, masses, calcifications, carcinomas and asymmetry mammograms.

Applying Deep Learning for Breast Cancer Detection in Radiology

“…Using deep convolutional neural networks (CNN), Khan et al [ 7 ] proposed a method for classifying and segmenting breast abnormalities, such as calcifications, masses, asymmetry and carcinomas. The images have been subjected to various filtering techniques in order to select the most appropriate one, including Wiener filters to reduce image noise, inverse filtering to recover blurred images and median filters to reduce the amount of intensity variation between pixels while maintaining the sharpness of image edges [ 61 ].…”

“…There are several ways to detect breast cancer. Breast self-examination can be conducted by pressing on the breast and checking for changes [ 7 ]. However, this method is not very reliable in detecting cancer.…”

Automatic Detection and Classification of Insects Using Hybrid FF-GWO-CNN Algorithm

“…Therefore, deep learning is applied to classify larger data sets and solve more complicated issues. Deep learning approaches based on Convolutional Neural Networks (CNN) [25][26][27][28] are widely utilized for efficient classification with increased accuracy and convergence speed. This classification approach is optimized by various statistical algorithms, among which Firefly (FF) [29] and Grey Wolf Optimization (GWO) [30] are utilized in this approach.…”

Classification and prediction for multi-cancer data with ultrahigh-dimensional gene expressions

“…The detailed and fundamental discussions of those methods can be found in [7,8], and were reviewed by [9] as well. In recent years, deep learning approaches, such as convolutional neural network (e.g., [10]) or natural language processing (e.g., [11]), have been developed to deal with multicalssification. More applications can be found in some monographs, such as [12][13][14].…”