Enhancing Ductal Carcinoma Classification Using Transfer Learning with 3D U-Net Models in Breast Cancer Imaging

Abstract

Breast cancer ranks among the leading causes of death for women globally, making it imperative to swiftly and precisely detect the condition to ensure timely treatment and enhanced chances of recovery. This study focuses on transfer learning with 3D U-Net models to classify ductal carcinoma, the most frequent subtype of breast cancer, in histopathology imaging. In this research work, a dataset of 162 microscopic images of breast cancer specimens is utilized for breast histopathology analysis. Preprocessing the original image data includes shrinking the images, standardizing the intensities, and extracting patches of size 50 × 50 pixels. The retrieved patches were employed to construct a basic 3D U-Net model and a refined 3D U-Net model that had been previously trained on an extensive medical image segmentation dataset. The findings revealed that the fine-tuned 3D U-Net model (97%) outperformed the simple 3D U-Net model (87%) in identifying ductal cancer in breast histopathology imaging. The fine-tuned model exhibited a smaller loss (0.003) on the testing data (0.041) in comparison to the simple model. The disparity in the training and testing accuracy reveals that the fine-tuned model may have overfitted to the training data indicating that there is room for improvement. To progress in computer-aided diagnosis, the research study also adopted various data augmentation methodologies. The experimental approach that was put forward achieved state-of-the-art performance, surpassing the benchmark techniques used in previous studies in the same field, and exhibiting greater accuracy. The presented scheme has promising potential for better cancer detection and diagnosis in practical applications of mammography.

Offloading the computational complexity of transfer learning with generic features

“…Every model has strengths and a brilliant record in image classification. These standard CNN architectures pre-trained on ImageNet are commonly used to accomplish transfer learning tasks ( Khalil et al, 2023b ). Every CNN model has millions of trainable parameters trained on the ImageNet dataset of 1,000 different classes.…”

“…The models have their strengths and certain advantages in image classification ( Du et al, 2023 ). The standard CNN architectures pre-trained on well-known datasets are commonly used to accomplish transfer learning tasks ( Khalil et al, 2023a ). The CNN model has millions of trainable parameters and thousands of classes.…”

“…Ducts are the most common spots where breast cancer attacks. Approximately 80% of all breast cancer cases are attributed to it, making it the most common type of breast cancer ( Khalil et al, 2023b ). In the sub-types of invasive breast cancer, invasive ductal carcinoma is the most dangerous and common kind of breast cancer that disturbs the milk ducts and nearby tissues ( Derakhshan & Reis-Filho, 2022 ; Diamantopoulou et al, 2022 ).…”

Advancing Content-Based Histopathological Image Retrieval Pre-Processing: A Comparative Analysis of the Effects of Color Normalization Techniques

“…Moving to the bottleneck, an attention block with a filter size of [32,16,1,64] is introduced to enhance the feature vectors by robustness to noise or occlusions. The decoder is made up of three Conv2DTranspose with [32,16,3]. The kernel size in this structure was fixed to 3 in all the layers.…”

“…For this large amount of patients, medication should be accurate with little to zero margin for error; otherwise, the consequences of a wrong diagnosis could be fatal [2]. Also, since breast cancer is one of the leading causes of death for women globally, precise detection and timely treatment can enhance the chances of recovery [3]. Computer-aided diagnosis (CAD) provides some deep learning (DL)-based techniques in digital pathology that can assist pathologists to make more accurate cancer diagnoses [4].…”