Semantic segmentation of breast cancer images using DenseNet with proposed PSPNet.

For early detection of cancer tumors, the semantic segmentation based technique is proposed because the existing numerous methods fail while classifying due to accuracy and ineffectual decision-making. Therefore, in this paper, the hybrid semantic segmentation networks are introduced. In the beginning, the input image sets are applied into the pre-processing phase and after that, subject to the process of segmentation. The pre-processing process of image contrast enhancement is done by Adaptive Local Gamma Correction (ALGC). The semantic segmentation topology is done by using the hybrid network of the DenseNet-121 model with Attention based pyramid scene parsing network (Att-PSPnet). The feature map extraction and scene parsing are handled by the Att-PSPnet network. The Attention Gate mechanism is introduced to improve the quality of the high-dimensional hidden layer features by highlighting the useful information, and unnecessary information and noise are suppressed. To make the efficient decision and enhance prediction accuracy, the pyramid dilated convolution module (PDM) is a branch of the attention-based pyramid pooling module that enlarges the receptive field to extract global information. Additionally, the global average pooling (GAP) layer is introduced at the output of the feature map. The performance of the proposed method is validated using the Google Colab environment with a histologically confirmed dataset. The experimental results are compared with existing methods like FCN, Unet, and PSPNet in terms of IoU, accuracy, precision, recall, F1 score, IoU, and more. The proposed method achieves 94.68% prediction accuracy which is higher than the existing approaches. [ABSTRACT FROM AUTHOR]