Automatic Grading System for Diagnosis of Breast Cancer Exploiting Co-occurrence Shearlet Transform and Histogram Features

Multi-Classification of Breast Cancer Lesions in Histopathological Images Using DEEP_Pachi: Multiple Self-Attention Head

INTRODUCTION AND BACKGROUND

Despite fast developments in the medical field, histological diagnosis is still regarded as the benchmark in cancer diagnosis. However, the input image feature extraction that is used to determine the severity of cancer at various magnifications is harrowing since manual procedures are biased, time consuming, labor intensive, and error-prone. Current state-of-the-art deep learning approaches for breast histopathology image classification take features from entire images (generic features). Thus, they are likely to overlook the essential image features for the unnecessary features, resulting in an incorrect diagnosis of breast histopathology imaging and leading to mortality.

METHODS

This discrepancy prompted us to develop DEEP_Pachi for classifying breast histopathology images at various magnifications. The suggested DEEP_Pachi collects global and regional features that are essential for effective breast histopathology image classification. The proposed model backbone is an ensemble of DenseNet201 and VGG16 architecture. The ensemble model extracts global features (generic image information), whereas DEEP_Pachi extracts spatial information (regions of interest). Statistically, the evaluation of the proposed model was performed on publicly available dataset: BreakHis and ICIAR 2018 Challenge datasets.

RESULTS

A detailed evaluation of the proposed model's accuracy, sensitivity, precision, specificity, and f1-score metrics revealed the usefulness of the backbone model and the DEEP_Pachi model for image classifying. The suggested technique outperformed state-of-the-art classifiers, achieving an accuracy of 1.0 for the benign class and 0.99 for the malignant class in all magnifications of BreakHis datasets and an accuracy of 1.0 on the ICIAR 2018 Challenge dataset.

CONCLUSIONS

The acquired findings were significantly resilient and proved helpful for the suggested system to assist experts at big medical institutions, resulting in early breast cancer diagnosis and a reduction in the death rate.

PdpaMn inhibits fatty acid synthase-mediated glycolysis by down-regulating PI3K/Akt signaling pathway in breast cancer

Novel manganese complex, PdpaMn ([(Pdpa)MnCl2]), was developed to induce apoptosis in breast cancer cells. The impact of phosphoinositide-(3)-kinase pathway onto fatty acid synthase (FASN) has an effect on cellular metabolism in breast cancer. However, reverse actions from FASN towards PI3K/Akt are still indefinable. Perhaps, loss of FASN could regulate glycolysis. Previously we established that PdpaMn inhibits FASN and involve in mitochondrial function. This study investigated the activity of PdpaMn on glycolysis and its mechanism. PdpaMn was used to suppress FASN expression in tumor. Expression of ATP and lactic acid level was measured to investigate the glycolysis variance in cells and animals. MCF-7 and 4T1 cells were treated with G28UCM, an inhibitor of FASN and PdpaMn, western blotting to detect PI3K/Akt signaling pathway. The capacity of proliferation was investigated by western blotting and immunohistochemistry. PdpaMn selectively inhibits cancer cells and tumor growth but also block FASN expression and suppresses the content of free fatty acid. Lactate dehydrogenase (LDHA) protein level was down-regulated as G28UCM and PdpaMn inhibited FASN, glucose transporter (Glut1), and pyruvate kinase (PKM2) proteins level were not affected. PI3K, p-Akt in the experimental group evidently declined compared to the control group. Proliferation was suppressed in FASN-arbitrated glycolysis. Our study supports the hypothesis that loss of FASN by PdpaMn suppressed glycolysis via down-regulating PI3K/Akt signaling pathway revealing the direct link between FASN and glycolysis. The results have paved the way to unravel the mechanisms of FASN and mitochondrial will be useful for designing novel co-targeting strategies for breast cancer.

Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm

BACKGROUND

Otitis media (OM) is the infection and inflammation of the mucous membrane covering the Eustachian with the airy cavities of the middle ear and temporal bone. OM is also one of the most common ailments. In clinical practice, the diagnosis of OM is carried out by visual inspection of otoscope images. This vulnerable process is subjective and error-prone.

METHODS

In this study, a novel computer-aided decision support model based on the convolutional neural network (CNN) has been developed. To improve the generalized ability of the proposed model, a combination of the channel and spatial model (CBAM), residual blocks, and hypercolumn technique is embedded into the proposed model. All experiments were performed on an open-access tympanic membrane dataset that consists of 956 otoscopes images collected into five classes.

RESULTS

The proposed model yielded satisfactory classification achievement. The model ensured an overall accuracy of 98.26%, sensitivity of 97.68%, and specificity of 99.30%. The proposed model produced rather superior results compared to the pre-trained CNNs such as AlexNet, VGG-Nets, GoogLeNet, and ResNets. Consequently, this study points out that the CNN model equipped with the advanced image processing techniques is useful for OM diagnosis. The proposed model may help to field specialists in achieving objective and repeatable results, decreasing misdiagnosis rate, and supporting the decision-making processes.