ResNet50-Based Effective Model for Breast Cancer Classification Using Histopathology Images

Breast cancer is considered an immense threat and one of the leading causes of mortality in females.It is curable only when detected at an early stage.A standard cancer diagnosis approach involves detection of cancer-related anomalies in tumour histopathology images.Detection depends on the accurate identification of the landmarks in the visual artefacts present in the slide images.Researchers are continuously striving to develop automatic machine-learning algorithms for processing medical images to assist in tumour detection.Nowadays,computerbased automated systems play an important role in cancer image analysis and help healthcare experts make rapid and correct inferences about the type of cancer.This study proposes an effective convolutional neural networkbased(CNN-based)model that exploits the transfer-learning technique for automatic image classification between malignant and benign tumour,using histopathology images.Resnet50 architecture has been trained on new dataset for feature extraction,and fully connected layers have been fine-tuned for achieving highest training,validation and test accuracies.The result illustrated state-of-the-art performance of the proposed model with highest training,validation and test accuracies as 99.70%,99.24%and 99.24%,respectively.Classification accuracy is increased by 0.66%and 0.2%when compared with similar recent studies on training and test data results.Average precision and F1 score have also improved,and receiver operating characteristic(RoC)area has been achieved to 99.1%.Thus,a reliable,accurate and consistent CNN model based on pre-built Resnet50 architecture has been developed.

Breast Pathological Image Classification Based on VGG16 Feature Concatenation

Breast cancer is one of the malignancies that endanger women’s health all over the world.Considering that there is some noise and edge blurring in breast pathological images,it is easier to extract shallow features of noise and redundant information when VGG16 network is used,which is affected by its relative shallow depth and small convolution kernel.To improve the pathological diagnosis of breast cancers,we propose a classification method for benign and malignant tumors in the breast pathological images which is based on feature concatenation of VGG16 network.First,in order to improve the problems of small dataset size and unbalanced data samples,the original BreakHis dataset is processed by data augmentation technologies,such as geometric transformation and color enhancement.Then,to reduce noise and edge blurring in breast pathological images,we perform bilateral filtering and denoising on the original dataset and sharpen the edge features by Sobel operator,which makes the extraction of shallow features by VGG16 model more accurate.Based on transfer learning,the network model trained with the expanded dataset is called VGG16-1,and another model trained with the image denoising and sharpening and mixed with the original dataset is called VGG16-2.The features extracted by VGG16-1 and VGG16-2 are concatenated,and then classified by support vector machine.The final experimental results show that the average accuracy is 98.44%,98.89%,98.30%and 97.47%,respectively,when the proposed method is tested with the breast pathological images of 40×,100×,200×and 400×on BreakHis dataset.