[Retracted] Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer

Abstract

The second largest cause of mortality worldwide is breast cancer, and it mostly occurs in women. Early diagnosis has improved further treatments and reduced the level of mortality. A unique deep learning algorithm is presented for predicting breast cancer in its early stages. This method utilizes numerous layers to retrieve significantly greater amounts of information from the source inputs. It could perform automatic quantitative evaluation of complicated image properties in the medical field and give greater precision and reliability during the diagnosis. The dataset of axillary lymph nodes from the breast cancer patients was collected from Erasmus Medical Center. A total of 1050 images were studied from the 850 patients during the years 2018 to 2021. For the independent test, data samples were collected for 100 images from 95 patients at national cancer institute. The existence of axillary lymph nodes was confirmed by pathologic examination. The feed forward, radial basis function, and Kohonen self-organizing are the artificial neural networks (ANNs) which are used to train 84% of the Erasmus Medical Center dataset and test the remaining 16% of the independent dataset. The proposed model performance was determined in terms of accuracy (Ac), sensitivity (Sn), specificity (Sf), and the outcome of the receiver operating curve (Roc), which was compared to the other four radiologists’ mechanism. The result of the study shows that the proposed mechanism achieves 95% sensitivity, 96% specificity, and 98% accuracy, which is higher than the radiologists’ models (90% sensitivity, 92% specificity, and 94% accuracy). Deep learning algorithms could accurately predict the clinical negativity of axillary lymph node metastases by utilizing images of initial breast cancer patients. This method provides an earlier diagnostic technique for axillary lymph node metastases in patients with medically negative changes in axillary lymph nodes.

Progression of Selected Parameters of the Clinical Profile of Patients with Periodontitis Using Kohonen’s Self-Organizing Maps

“…The Kohonen self-organizing algorithms, feed forward, and radial basis functions are examples of assessment techniques for artificial neural networks. The outcomes of the study indicate that the deep learning model can more accurately assess the final diagnosis of the axillary lymph node metastatic from US imaging of initial breast cancer [ 42 ]. Kohonen’s artificial neural networks were also used to select new inhibitors of SARS-CoV-2 activator protein furin.…”

“…In the dermatologic study of Styła et al the dermatoscopic images were used to train Kohonen neural networks to provide fully automatic diagnostic systems capable of determining the type of pigmented skin lesion [ 48 ]. Referring to the above and recent studies, it was shown that machine learning algorithms, particularly Kohonen networks, might be useful in medicine and can improve diagnosis and give clinicians more tools in treatment planning [ 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. According to our study, we can recommend other specialists use Kohonen networks in their daily practice to ease the prediction of the progression of periodontitis with the usage of data: gender, age, active nicotinism, the number of teeth still present, the approximate plaque index (API), bleeding on probing (BoP), pocket depth (PD), and clinical attachment loss.…”

Application of artificial intelligence in predicting lymph node metastasis in breast cancer

“…Other retrospective studies investigating the prediction of lymph node metastasis in breast cancer patients using ultrasound images and AI algorithms to evaluate the images prove more robust in their methodologies through enrolling larger numbers of patients into their training and validation sets. A study by Ashokkumar et al evaluated three different deep Artificial Neural Networks (ANNs)—one based on feed forward, one on radial basis function, and one on Kohonen self-organizing models—for use in predicting metastasis in pre-operative breast cancer patients and compared their performance against experienced radiologists ( 19 ). The study involved a total of 908 images from 750 patients for training the sets.…”

Integrated Ensemble Strategy for Breast Cancer Detection using Dimensionally Reduction Technique

“…Millions of people worldwide suffer from deadly breast cancer [1] Early detection and accurate diagnosis are crucial factors in improving the prognosis and overall survival rates of breast cancer patients. In recent years, the intersection of healthcare and data science has led to the development of novel techniques and methodologies aimed at enhancing the accuracy and efficiency of breast cancer detection [2]. One promising avenue in this endeavour is the application of DR techniques which are designed to reduce the complexity of high-dimensional data while preserving essential information [3].…”