Automated Bi-Fold Weighted Ensemble Algorithms and its Application to Brain Tumor Detection and Classification

• URL: http://arxiv.org/abs/2404.00576v1
• Date: Sun, 31 Mar 2024 06:38:08 GMT
• Title: Automated Bi-Fold Weighted Ensemble Algorithms and its Application to Brain Tumor Detection and Classification
• Authors: PoTsang B. Huang, Muhammad Rizwan, Mehboob Ali,
• Abstract summary: Brain tumors pose significant challenges, especially in third-world countries. Early diagnosis plays a vital role in effectively managing brain tumors and reducing mortality rates. We present two cutting-edge bi-fold weighted voting ensemble models that aim to boost the effectiveness of weighted ensemble methods.
• Score: 0.3413711585591077
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The uncontrolled and unstructured growth of brain cells is known as brain tumor, which has one of the highest mortality rates among diseases from all types of cancers. Due to limited diagnostic and treatment capabilities, they pose significant challenges, especially in third-world countries. Early diagnosis plays a vital role in effectively managing brain tumors and reducing mortality rates. However, the availability of diagnostic methods is hindered by various limitations, including high costs and lengthy result acquisition times, impeding early detection of the disease. In this study, we present two cutting-edge bi-fold weighted voting ensemble models that aim to boost the effectiveness of weighted ensemble methods. These two proposed methods combine the classification outcomes from multiple classifiers and determine the optimal result by selecting the one with the highest probability in the first approach, and the highest weighted prediction in the second technique. These approaches significantly improve the overall performance of weighted ensemble techniques. In the first proposed method, we improve the soft voting technique (SVT) by introducing a novel unsupervised weight calculating schema (UWCS) to enhance its weight assigning capability, known as the extended soft voting technique (ESVT). Secondly, we propose a novel weighted method (NWM) by using the proposed UWCS. Both of our approaches incorporate three distinct models: a custom-built CNN, VGG-16, and InceptionResNetV2 which has been trained on publicly available datasets. The effectiveness of our proposed systems is evaluated through blind testing, where exceptional results are achieved. We then establish a comparative analysis of the performance of our proposed methods with that of SVT to show their superiority and effectiveness.

Related papers

• Enhanced Deep Learning Methodologies and MRI Selection Techniques for Dementia Diagnosis in the Elderly Population [5.103059984821972]
  We introduce a novel methodology for the classification of demented and non-demented elderly patients using 3D brain Magnetic Resonance Imaging (MRI) scans. Our approach features a unique technique for selectively processing MRI slices, focusing on the most relevant brain regions and excluding less informative sections. This methodology is complemented by a confidence-based classification committee composed of three custom deep learning models.
  arXiv  Detail & Related papers  (2024-07-24T14:48:40Z)
• Two new feature selection methods based on learn-heuristic techniques for breast cancer prediction: A comprehensive analysis [6.796017024594715]
  We suggest two novel feature selection (FS) methods based upon an imperialist competitive algorithm (ICA) and a bat algorithm (BA) This study aims to enhance diagnostic models' efficiency and present a comprehensive analysis to help clinical physicians make much more precise and reliable decisions than before.
  arXiv  Detail & Related papers  (2024-07-19T19:07:53Z)
• UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training [66.16134293168535]
  We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
  arXiv  Detail & Related papers  (2023-09-13T09:22:49Z)
• An Intelligent Decision Support Ensemble Voting Model for Coronary Artery Disease Prediction in Smart Healthcare Monitoring Environments [0.0]
  Coronary artery disease (CAD) is one of the most common cardiac diseases worldwide. E-diagnosis tool based on machine learning (ML) algorithms can be used in a smart healthcare monitoring system.
  arXiv  Detail & Related papers  (2022-10-25T21:09:34Z)
• NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization [59.15047491202254]
  symptom checking systems inquire users for their symptoms and perform a rapid and affordable medical assessment of their condition. We propose a new approach based on the supervised learning of neural models with logic regularization. Our experiments show that the proposed approach outperforms the best existing methods in the accuracy of diagnosis when the number of diagnoses and symptoms is large.
  arXiv  Detail & Related papers  (2022-06-02T07:57:17Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• SSD-KD: A Self-supervised Diverse Knowledge Distillation Method for Lightweight Skin Lesion Classification Using Dermoscopic Images [62.60956024215873]
  Skin cancer is one of the most common types of malignancy, affecting a large population and causing a heavy economic burden worldwide. Most studies in skin cancer detection keep pursuing high prediction accuracies without considering the limitation of computing resources on portable devices. This study specifically proposes a novel method, termed SSD-KD, that unifies diverse knowledge into a generic KD framework for skin diseases classification.
  arXiv  Detail & Related papers  (2022-03-22T06:54:29Z)
• An explainable two-dimensional single model deep learning approach for Alzheimer's disease diagnosis and brain atrophy localization [3.9281410693767036]
  We propose an end-to-end deep learning approach for automated diagnosis of Alzheimer's disease (AD) and localization of important brain regions related to the disease from sMRI data. Our approach has been evaluated on two publicly accessible datasets for two classification tasks of AD vs. cognitively normal (CN) and progressive MCI (pMCI) vs. stable MCI (sMCI) The experimental results indicate that our approach outperforms the state-of-the-art approaches, including those using multi-model and 3D CNN methods.
  arXiv  Detail & Related papers  (2021-07-28T07:19:00Z)
• Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification [63.44396343014749]
  We propose a new margin-based surrogate loss function for the AUC score. It is more robust than the commonly used. square loss while enjoying the same advantage in terms of large-scale optimization. To the best of our knowledge, this is the first work that makes DAM succeed on large-scale medical image datasets.
  arXiv  Detail & Related papers  (2020-12-06T03:41:51Z)
• Learning Binary Semantic Embedding for Histology Image Classification and Retrieval [56.34863511025423]
  We propose a novel method for Learning Binary Semantic Embedding (LBSE) Based on the efficient and effective embedding, classification and retrieval are performed to provide interpretable computer-assisted diagnosis for histology images. Experiments conducted on three benchmark datasets validate the superiority of LBSE under various scenarios.
  arXiv  Detail & Related papers  (2020-10-07T08:36:44Z)
• Automatic Breast Lesion Classification by Joint Neural Analysis of Mammography and Ultrasound [1.9814912982226993]
  We propose a deep-learning based method for classifying breast cancer lesions from their respective mammography and ultrasound images. The proposed approach is based on a GoogleNet architecture, fine-tuned for our data in two training steps. It achieves an AUC of 0.94, outperforming state-of-the-art models trained over a single modality.
  arXiv  Detail & Related papers  (2020-09-23T09:08:24Z)

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.