Breast cancer has become a symbol of tremendous concern in the modern world, as it is one of the major causes of cancer mortality worldwide. In this regard, breast ultrasonography images are frequently utilized by doctors to diagnose breast cancer at an early stage. However, the complex artifacts and heavily noised breast ultrasonography images make diagnosis a great challenge. Furthermore, the ever-increasing number of patients being screened for breast cancer necessitates the use of automated end-to-end technology for highly accurate diagnosis at a low cost and in a short time. In this concern, to develop an end-to-end integrated pipeline for breast ultrasonography image classification, we conducted an exhaustive analysis of image preprocessing methods such as K Means++ and SLIC, as well as four transfer learning models such as VGG16, VGG19, DenseNet121, and ResNet50. With a Dice-coefficient score of 63.4 in the segmentation stage and accuracy and an F1-Score (Benign) of 73.72 percent and 78.92 percent in the classification stage, the combination of SLIC, UNET, and VGG16 outperformed all other integrated combinations. Finally, we have proposed an end to end integrated automated pipelining framework which includes preprocessing with SLIC to capture super-pixel features from the complex artifact of ultrasonography images, complementing semantic segmentation with modified U-Net, leading to breast tumor classification using a transfer learning approach with a pre-trained VGG16 and a densely connected neural network. The proposed automated pipeline can be effectively implemented to assist medical practitioners in making more accurate and timely diagnoses of breast cancer.
Breast cancer has been identified as one of the most common invasive cancers and the second leading cause of cancer death among women. The survival rates have, however, improved dramatically in recent years, thanks to the advances in the screening and treatment process, hugely depending on how early the disease was detected. Along with the physicians, this had also initiated researchers all over the globe to dedicate themselves to extensive research to produce automated diagnosis strategies for breast cancer. Realizing the extraordinary potential of machine learning-based models in the biomedical domain, a large number of diagnosis methods have been proposed in this direction. In our study, we propose a hybrid unique machine learning framework that integrates individual prediction probabilities from 3 machine learning (Logistic Regression, Support Vector Machine, and K Nearest Neighbors) classifiers, then enhances the performance of these 3 classifiers through hybridization, stacking a gradient boosting algorithm over the combination of these classifiers which ultimately results in a 10 Fold Cross Validation Score of 98.4%, Recall of 100% and Precision of 97.3%. Besides, to handle the class imbalance problem we have incorporated SMOTE(Synthetic Minority Oversampling Technique) for minority classes and also Robust Scaling for normalization to deal with outliers in the dataset. In our proposed hybrid solution, we successfully adopted the breast cancer domain in every stage of our framework, starting from data pre-processing, feature extraction and finally classification. Our framework outperformed some recent state of the art studies in the breast cancer domain.
Artificial Intelligence in Medical Image Segmentation and Classification