A comprehensive study on Blood Cancer detection and classification using Convolutional Neural Network

• URL: http://arxiv.org/abs/2409.06689v1
• Date: Tue, 10 Sep 2024 17:53:47 GMT
• Title: A comprehensive study on Blood Cancer detection and classification using Convolutional Neural Network
• Authors: Md Taimur Ahad, Sajib Bin Mamun, Sumaya Mustofa, Bo Song, Yan Li,
• Abstract summary: This study develops a novel ensemble model DIX to detect and classify blood cancer. The statistical result suggests that DIX outperformed the original and transfer learning performance, providing an accuracy of 99.12%. The high accuracy in detecting and categorization blood cancer detection using CNN suggests that the CNN model is promising in blood cancer disease detection.
• Score: 6.648024246537002
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over the years in object detection several efficient Convolutional Neural Networks (CNN) networks, such as DenseNet201, InceptionV3, ResNet152v2, SEresNet152, VGG19, Xception gained significant attention due to their performance. Moreover, CNN paradigms have expanded to transfer learning and ensemble models from original CNN architectures. Research studies suggest that transfer learning and ensemble models are capable of increasing the accuracy of deep learning (DL) models. However, very few studies have conducted comprehensive experiments utilizing these techniques in detecting and localizing blood malignancies. Realizing the gap, this study conducted three experiments; in the first experiment -- six original CNNs were used, in the second experiment -- transfer learning and, in the third experiment a novel ensemble model DIX (DenseNet201, InceptionV3, and Xception) was developed to detect and classify blood cancer. The statistical result suggests that DIX outperformed the original and transfer learning performance, providing an accuracy of 99.12%. However, this study also provides a negative result in the case of transfer learning, as the transfer learning did not increase the accuracy of the original CNNs. Like many other cancers, blood cancer diseases require timely identification for effective treatment plans and increased survival possibilities. The high accuracy in detecting and categorization blood cancer detection using CNN suggests that the CNN model is promising in blood cancer disease detection. This research is significant in the fields of biomedical engineering, computer-aided disease diagnosis, and ML-based disease detection.

Related papers

• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• DVS: Blood cancer detection using novel CNN-based ensemble approach [7.13230591574577]
  Blood cancer can only be diagnosed properly if it is detected early. This study investigates the efficacy and suitability of modern Convolutional Neural Network (CNN) architectures for the detection and classification of blood malignancies. The ensemble DVS model is the best for detecting and classifying blood cancers.
  arXiv  Detail & Related papers  (2024-09-12T09:16:24Z)
• A study on Deep Convolutional Neural Networks, Transfer Learning and Ensemble Model for Breast Cancer Detection [2.5748316361772963]
  This study compares the performance of D-CNN, which includes the original CNN, transfer learning, and an ensemble model, in detecting breast cancer. The ensemble model provides the highest detection and classification accuracy of 99.94% for breast cancer detection and classification. The high accuracy in detecting and categorising breast cancer detection using CNN suggests that the CNN model is promising in breast cancer disease detection.
  arXiv  Detail & Related papers  (2024-09-10T17:58:21Z)
• A study on deep feature extraction to detect and classify Acute Lymphoblastic Leukemia (ALL) [0.0]
  Acute lymphoblastic leukaemia (ALL) is a blood malignancy that mainly affects adults and children. This study looks into the use of deep learning, specifically Convolutional Neural Networks (CNNs) for the detection and classification of ALL. With an 87% accuracy rate, the ResNet101 model produced the best results, closely followed by DenseNet121 and VGG19.
  arXiv  Detail & Related papers  (2024-09-10T17:53:29Z)
• Analysis of Modern Computer Vision Models for Blood Cell Classification [49.1574468325115]
  This study uses state-of-the-art architectures, including MaxVit, EfficientVit, EfficientNet, EfficientNetV2, and MobileNetV3 to achieve rapid and accurate results. Our approach not only addresses the speed and accuracy concerns of traditional techniques but also explores the applicability of innovative deep learning models in hematological analysis.
  arXiv  Detail & Related papers  (2024-06-30T16:49:29Z)
• Diagnosis of Skin Cancer Using VGG16 and VGG19 Based Transfer Learning Models [0.6827423171182154]
  Deep convolution neural networks (CNN) have shown an excellent potential for data and image classification. In this article, we inspect skin lesion classification problem using CNN techniques. We present that prominent classification accuracy of lesion detection can be obtained by proper designing and applying of transfer learning framework.
  arXiv  Detail & Related papers  (2024-04-01T15:06:20Z)
• COVID-19 Detection using Transfer Learning with Convolutional Neural Network [0.0]
  COVID-19 is a fatal infectious disease, first recognized in December 2019 in Wuhan, Hubei, China. In this study, a Transfer learning strategy (CNN) for detecting COVID-19 infection from CT images has been proposed. In the proposed model, a multilayer Convolutional neural network (CNN) with Transfer learning model Inception V3 has been designed.
  arXiv  Detail & Related papers  (2022-06-17T05:30:14Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• 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)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)
• CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks [51.589769497681175]
  The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
  arXiv  Detail & Related papers  (2020-07-20T21:47:28Z)

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.