Bimodal Distribution Removal and Genetic Algorithm in Neural Network for Breast Cancer Diagnosis

• URL: http://arxiv.org/abs/2002.08729v1
• Date: Thu, 20 Feb 2020 13:51:40 GMT
• Title: Bimodal Distribution Removal and Genetic Algorithm in Neural Network for Breast Cancer Diagnosis
• Authors: Ke Quan
• Abstract summary: This paper examines the effectiveness of Bimodal Distribution Removal (BDR) against the target cancer diagnosis classification problem. BDR process in fact negatively impacts classification performance. This paper also explores genetic algorithm as an efficient tool for feature selection.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diagnosis of breast cancer has been well studied in the past. Multiple linear programming models have been devised to approximate the relationship between cell features and tumour malignancy. However, these models are less capable in handling non-linear correlations. Neural networks instead are powerful in processing complex non-linear correlations. It is thus certainly beneficial to approach this cancer diagnosis problem with a model based on neural network. Particularly, introducing bias to neural network training process is deemed as an important means to increase training efficiency. Out of a number of popular proposed methods for introducing artificial bias, Bimodal Distribution Removal (BDR) presents ideal efficiency improvement results and fair simplicity in implementation. However, this paper examines the effectiveness of BDR against the target cancer diagnosis classification problem and shows that BDR process in fact negatively impacts classification performance. In addition, this paper also explores genetic algorithm as an efficient tool for feature selection and produced significantly better results comparing to baseline model that without any feature selection in place

Related papers

• Enhancing Eye Disease Diagnosis with Deep Learning and Synthetic Data Augmentation [0.0]
  In this paper, an ensemble learning technique is proposed for early detection and management of diabetic retinopathy. The proposed model is tested on the APTOS dataset and it is showing supremacy on the validation accuracy ($99%)$ in comparison to the previous models.
  arXiv  Detail & Related papers  (2024-07-25T04:09:17Z)
• Segmentation of Non-Small Cell Lung Carcinomas: Introducing DRU-Net and Multi-Lens Distortion [0.1935997508026988]
  We are proposing a segmentation model (DRU-Net) that can provide a delineation of human non-small cell lung carcinomas. We have used two datasets (Norwegian Lung Cancer Biobank and Haukeland University Hospital lung cancer cohort) to create our proposed model. The proposed spatial augmentation method (multi-lens distortion) improved the network performance by 3%.
  arXiv  Detail & Related papers  (2024-06-20T13:14:00Z)
• Breast Cancer Image Classification Method Based on Deep Transfer Learning [40.3927727959038]
  A breast cancer image classification model algorithm combining deep learning and transfer learning is proposed. Experimental results demonstrate that the algorithm achieves an efficiency of over 84.0% in the test set, with a significantly improved classification accuracy compared to previous models.
  arXiv  Detail & Related papers  (2024-04-14T12:09:47Z)
• Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning [55.789874096142285]
  We present a deep learning-based brain tumor recurrence location prediction network. We first train a multi-modal brain tumor segmentation network on the public dataset BraTS 2021. Then, the pre-trained encoder is transferred to our private dataset for extracting the rich semantic features. Two decoders are constructed to jointly segment the present brain tumor and predict its future tumor recurrence location.
  arXiv  Detail & Related papers  (2023-04-11T02:45:38Z)
• Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models [0.0]
  We propose the integration of expert-derived radiomics and DNN-predicted biomarkers in interpretable classifiers. In our evaluation and practical application, the only input to ConRad is a segmented CT scan. Overall, the proposed ConRad model combines CBM-derived biomarkers and radiomics features in an interpretable ML model which perform excellently for the lung malignancy classification.
  arXiv  Detail & Related papers  (2023-03-20T15:00:52Z)
• Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
  We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe. GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
  arXiv  Detail & Related papers  (2023-02-06T16:38:43Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• A Novel and Efficient Tumor Detection Framework for Pancreatic Cancer via CT Images [21.627818410241552]
  A novel and efficient pancreatic tumor detection framework is proposed in this paper. The contribution of the proposed method mainly consists of three components: Augmented Feature Pyramid networks, Self-adaptive Feature Fusion and a Dependencies Computation Module. Experimental results achieve competitive performance in detection with the AUC of 0.9455, which outperforms other state-of-the-art methods to our best of knowledge.
  arXiv  Detail & Related papers  (2020-02-11T15:48:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.