A cost-effective computer-vision based breast cancer diagnosis

Optimal deep transfer learning driven computer‐aided breast cancer classification using ultrasound images

Breast cancer (BC) is regarded as the second leading type of cancer among women globally. Ultrasound images are typically used for the identification and classification of abnormalities that exist in the breast. To enhance diagnosis performance, the computer assisted diagnosis (CAD) model finds it effective for identifying and classifying BC. Generally, the CAD technique contains distinct procedures like feature extraction, preprocessing, segmentation, and classification. The recent developments of deep learning (DL) algorithms in the form of CAD system helps to minimize the cost and enhance the ability of radiologists to interpret medical images. Therefore, this study develops an optimal deep transfer learning driven computer aided BC classification (ODTLD‐CABCC) technique on ultrasound images. The presented ODTLD‐CABCC algorithm undergoes pre‐processing in two levels such as median filtering based noise removal and graph cut segmentation. Furthermore, the residual network (ResNet101) model can be used as a feature extractor. Finally, the sailfish optimizer (SFO) with a labelled weighted extreme learning machine (LWELM) algorithm is used for the classification process. The SFO technique is employed to choose optimal parameters involved in the LWELM algorithm. A comprehensive set of simulations are conducted on the benchmark data and the experimental outcomes are examined under numerous aspects. The comparative examination represents the supremacy of the ODTLD‐CABCC technique over the other approaches.

Fully convolutional network for automated detection and diagnosis of mammographic masses

Breast cancer, though rare in male, is very frequent in female and has high mortality rate which can be reduced if detected and diagnosed at the early stage. Thus, in this paper, deep learning architecture based on U-Net is proposed for the detection of breast masses and its characterization as benign or malignant. The evaluation of the proposed architecture in detection is carried out on two benchmark datasets- INbreast and DDSM and achieved a true positive rate of 99.64% at 0.25 false positives per image for INbreast dataset while the same for DDSM are 97.36% and 0.38 FPs/I, respectively. For mass characterization, an accuracy of 97.39% with an AUC of 0.97 is obtained for INbreast while the same for DDSM are 96.81%, and 0.96, respectively. The measured results are further compared with the state-of-the-art techniques where the introduced scheme takes an edge over others.

Automated carcinoma classification using efficient nuclei-based patch selection and deep learning techniques

Breast cancer can be successfully treated if diagnosed at its earliest, though it is considered as a fatal disease among women. The histopathology slide turned images are the gold standard for tumor diagnosis. However, the manual diagnosis is still tedious due to its structural complexity. With the advent of computer-aided diagnosis, time and computation intensive manual procedure can be managed with the development of an automated classification system. The feature extraction and classification are quite challenging as these images involve complex structures and overlapping nuclei. A novel nuclei-based patch extraction method is proposed for the extraction of non-overlapping nuclei patches obtained from the breast tumor dataset. An ensemble of pre-trained models is used to extract the discriminating features from the identified and augmented non-overlapping nuclei patches. The discriminative features are further fused using p-norm pooling technique and are classified using a LightGBM classifier with 10-fold cross-validation. The obtained results showed an increase in the overall performance in terms of accuracy, sensitivity, specificity, and precision. The proposed framework yielded an accuracy of 98.3% for binary class classification and 95.1% for multi-class classification on ICIAR 2018 dataset.

Automatic detection of breast cancer in ultrasound images using Mayfly algorithm optimized handcrafted features

BACKGROUND

The incidence rates of breast cancer in women community is progressively raising and the premature diagnosis is necessary to detect and cure the disease.

OBJECTIVE

To develop a novel automated disuse detection framework to examine the Breast-Ultrasound-Images (BUI).

METHODS

This scheme includes the following stages; (i) Image acquisition and resizing, (ii) Gaussian filter-based pre-processing, (iii) Handcrafted features extraction, (iv) Optimal feature selection with Mayfly Algorithm (MA), (v) Binary classification and validation. The dataset includes BUI extracted from 133 normal, 445 benign and 210 malignant cases. Each BUI is resized to 256×256×1 pixels and the resized BUIs are used to develop and test the new scheme. Handcrafted feature-based cancer detection is employed and the parameters, such as Entropies, Local-Binary-Pattern (LBP) and Hu moments are considered. To avoid the over-fitting problem, a feature reduction procedure is also implemented with MA and the reduced feature sub-set is used to train and validate the classifiers developed in this research.

RESULTS

The experiments were performed to classify BUIs between (i) normal and benign, (ii) normal and malignant, and (iii) benign and malignant cases. The results show that classification accuracy of > 94%, precision of > 92%, sensitivity of > 92% and specificity of > 90% are achieved applying the developed new schemes or framework.

CONCLUSION

In this work, a machine-learning scheme is employed to detect/classify the disease using BUI and achieves promising results. In future, we will test the feasibility of implementing deep-learning method to this framework to further improve detection accuracy.