Abstract
A new deep learning-based classification model called the Stochastic Dilated Residual Ghost (SDRG) was proposed in this work for categorizing histopathology images of breast cancer. The SDRG model used the proposed Multiscale Stochastic Dilated Convolution (MSDC) model, a ghost unit, stochastic upsampling, and downsampling units to categorize breast cancer accurately. This study addresses four primary issues: first, strain normalization was used to manage color divergence, data augmentation with several factors was used to handle the overfitting. The second challenge is extracting and enhancing tiny and low-level information such as edge, contour, and color accuracy; it is done by the proposed multiscale stochastic and dilation unit. The third contribution is to remove redundant or similar information from the convolution neural network using a ghost unit. According to the assessment findings, the SDRG model scored overall 95.65 percent accuracy rates in categorizing images with a precision of 99.17 percent, superior to state-of-the-art approaches.
Highlights
Breast Cancer (BC) is the primary reason behind the death of women globally and is answerable for numerous deaths every year (Afifi et al, 2019)
Total 7909 BreakHis samples and 3,078 BreCaHAD images were augmented with 19 parameters for magnification factors (40x, 100x, 200x, and 400x), for a total of 1,53,349
The proposed spatial dilated convolution unit, proposed channel dilated convolution unit, and stochastic dilated convolution unit make up the multiscale stochastic dilated convolution model, which increases tiny and low-level properties such as tip, contour, and color precision
Summary
Breast Cancer (BC) is the primary reason behind the death of women globally and is answerable for numerous deaths every year (Afifi et al, 2019). BC cases are rising daily, and patients are increasing each day; health sciences are still troubled by the upper and proper prognosis of cancer and deep learning. As per the medical specialty reports, carcinoma breast cancer is hard to hunt out at the initial stage; the survival rate can increase if detected at the initial stage. Numerous methods and models have developed, and deep learning involvement gives rise to cancer’s accurate and fast prognosis (Pacilè et al, 2020). CNN adds value to deep learning techniques that have shown to be effective in breast cancer detection and classification. A CNN model and its layered architecture recognize cancer and support medical science with the best outcomes (Choi et al, 2020). A convolution operation plays a keen role in the CNN, as shown in equation 1:
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