A Novel CNN-Inception-V4-Based Hybrid Approach for Classification of Breast Cancer in Mammogram Images

Abstract

Breast cancer is the most frequent disease in women, with one in every 19 women at risk. Breast cancer is the fifth leading cause of cancer death in women around the world. The most effective and efficient technique of controlling cancer development is early identification. Mammography helps in the early detection of cancer, which saves lives. Many studies conducted various tests to categorize the tumor and obtained positive findings. However, there are certain limits. Mass categorization in mammography is still a problem, although it is critical in aiding radiologists in establishing correct diagnoses. The purpose of this study is to develop a unique hybrid technique to identify breast cancer mass pictures as benign or malignant. The combination of two networks helps accelerate the categorization process. This study proposes a novel-based hybrid approach, CNN-Inception-V4, based on the fusing of these two networks. Mass images are used in this research from the CBIS-DDSM dataset. 450 images are taken for benign, and 450 images are used for malignant. The images are first cleaned by removing pectoral muscles, labels, and white borders. Then, CLAHE is used to these images to improve their quality in order to produce promising classification results. Following preprocessing, our model classifies cancer in mammography pictures as benign or malignant abnormalities. Our proposed model’s accuracy is 99.2%, with sensitivity of 99.8%, specificity of 96.3%, and F1-score of 97%. We also compared our proposed model to CNN, Inception-V4, and ResNet-50. Our proposed model outperforms existing classification models, according to the results.

Integration of feature enhancement technique in Google inception network for breast cancer detection and classification

“…Nazir et al [14] proposed a novel hybrid approach for classifying breast cancer in mammogram images. The approach combines two convolutional neural networks (CNNs): a CNN-Inception-V4 network and a CNN-ResNet-50 network.…”

“…Accordingly, through filliping, and rotation at 45, 90, 135, 180, 270, and 360 degrees, we increased the size of the normal dataset from 266 to1862, the malignant dataset from 421 to 2947, and finally the size of the benign dataset from 891 to 6237. Then, we divided it into training, validation, and test datasets and transfer the learned parameters with the locally collected dataset [14]. In addition to the Kaggle dataset.…”

“…However, most of the features are lost in each intermediate output (layer) of CNN, not on some layers of CNN. Due to this, the overall accuracy of CNN-based breast cancer detection is not as intended as possible [ 13,14]. In this research, we proposed a feature enhancement method that aims to enhance the intermediate output of CNN.…”

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

“…In [19], new algorithms for classification were presented and also mechanisms were included for minimizing breast cancer risk globally. Yet another method to accelerate the cancer detection process was designed in [20] employing hybrid method. Here, fusion of classification methods were employed therefore improving sensitivity and specificity to a greater extent.…”

Computational Framework of Inverted Fuzzy C-Means and Quantum Convolutional Neural Network Towards Accurate Detection of Ovarian Tumors

“…Inception-v4 is a version of Inception without any residual connections. Although Inception-v3 has comparatively lesser Inception modules, Inception-v4 has a simpler architecture (Ba Alawi et al, 2021;Nazir et al, 2022).…”