rewrite this in very formal language:
In [18], a novel hybrid AlexNet-gated recurrent unit (GRU) model is proposed for lymph
node (LN) breast cancer detection and classification using the well-known Kaggle (PCam)
dataset. The proposed model is compared with two other models, CNN-GRU and CNN-LSTM,
and achieves significantly better performance metrics such as accuracy, precision, sensitivity,
and specificity (99.50%, 98.10%, 98.90%, and 97.50%, respectively). The proposed model also
outperforms other recent ML/DL algorithms, demonstrating its superiority in LN breast cancer
detection and classification. Additionally, the proposed AlexNet-GRU model is computationally
efficient, making it a promising tool for clinical use in reducing pathologist errors during
the diagnosis process.
The aim of[19] is to provide an overview of the methodologies used for deep learning-based
image analysis of breast histopathology. The paper analyzes commonly used histopathology
image datasets, including BreaKHis, MITOS dataset, Camelyon, and others. Additionally, the
paper examines various performance metrics used to evaluate the effectiveness of breast cancer
prediction algorithms. Overall, the study reviews current deep learning models for the detection
and classification of breast cancer using histopathological images.
In[20] reviews the various methods that have been applied to develop machine learning algorithms
for the detection of cancer, focusing on the four most common and prevalent cancers
worldwide: lung, breast, prostate, and colorectal cancer. Additionally, the study proposes a
new methodology for detecting cancer using raw DNA sequences of matched tumor/normal
pairs as the only input. The proposed method utilizes state-of-the-art sentence transformers,
SBERT and SimCSE, to learn DNA representations, which are then sent to machine learning
algorithms, including XGBoost, Random Forest, LightGBM, and CNNs, for classification. The
results indicate that the XGBoost model, utilizing SBERT and SimCSE embeddings, achieved improving the performance of machine learning models. Overall, this study highlights the potential
of incorporating DNA sequence data in cancer detection and the effectiveness of machine
learning algorithms in accurately classifying cancer types.
an overall accuracy of 73 ± 0.13%and 75 ± 0.12 %, respectively, with the latter only marginally