Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach

Abstract

Introduction: Of all the cancers that afflict women, breast cancer (BC) has the second-highest mortality rate, and it is also believed to be the primary cause of the high death rate. Breast cancer is the most common cancer that affects women globally. There are two types of breast tumors: benign (less harmful and unlikely to become breast cancer) and malignant (which are very dangerous and might result in aberrant cells that could result in cancer).Methods: To find breast abnormalities like masses and micro-calcifications, competent and educated radiologists often examine mammographic images. This study focuses on computer-aided diagnosis to help radiologists make more precise diagnoses of breast cancer. This study aims to compare and examine the performance of the proposed shallow convolutional neural network architecture having different specifications against pre-trained deep convolutional neural network architectures trained on mammography images. Mammogram images are pre-processed in this study's initial attempt to carry out the automatic identification of BC. Thereafter, three different types of shallow convolutional neural networks with representational differences are then fed with the resulting data. In the second method, transfer learning via fine-tuning is used to feed the same collection of images into pre-trained convolutional neural networks VGG19, ResNet50, MobileNet-v2, Inception-v3, Xception, and Inception-ResNet-v2.Results: In our experiment with two datasets, the accuracy for the CBIS-DDSM and INbreast datasets are 80.4%, 89.2%, and 87.8%, 95.1% respectively.Discussion: It can be concluded from the experimental findings that the deep network-based approach with precise tuning outperforms all other state-of-the-art techniques in experiments on both datasets.

Laplacian Metropolis Hastings Gradient Torgerson Scaling and Optimized Vgg16 Based Mammogram Classification

“…However precision was said to be lacking. To address on this precision aspects, Deep CNN was designed in [13] for breast cancer detection. Over the past few years, the utilization of CAD system has become prevalent to improve accuracy in several medical domains.…”

“…From results (13) if the value of '𝑆𝑅 + ', is '0' then the mammogram images is said to be normal. On the other hand if the value of ' 𝑆𝑅 + ' ' 𝑙𝑖𝑒𝑠 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 0.1 𝑎𝑛𝑑 0.5 ' then mammogram images are benign or else it is said to be malignant.…”

Machine learning and new insights for breast cancer diagnosis

“…28 This current review extracted and organized the data in tabular form and summarized the use of MG in the diagnosis of breast cancer (Table 1). 2,4,5,7,9,24,29–79…”

Impact the Classes’ number on the convolutional neural networks performance for image classification

“…It is widely used in various real-life problem-solving scenarios, particularly in the field of computer vision to solve real-world problems like image classification [5], where CNN excels at image classification tasks because it can accurately identify and categorize objects within images. It has been used in applications such as autonomous driving (to detect pedestrians, traffic signs, and other vehicles) [6], medical imaging (to diagnose diseases from scans) [7], [8], [9] and facial recognition systems [10]. Also, it can perform pixel-level segmentation for semantic segmentation, where each pixel in an image is classified into specific classes or categories.…”