Histopathological Cancer Detection Using Hybrid Quantum Computing

• URL: http://arxiv.org/abs/2302.04633v1
• Date: Tue, 7 Feb 2023 03:02:33 GMT
• Title: Histopathological Cancer Detection Using Hybrid Quantum Computing
• Authors: Reek Majumdar, Biswaraj Baral, Bhavika Bhalgamiya, Taposh Dutta Roy
• Abstract summary: We present an effective application of quantum machine learning in the field of healthcare. For 1000 images with Resnet18, Hybrid Quantum and Classical (HQC) provided a slightly better accuracy of 88.5 percent than classical of 88.0 percent.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present an effective application of quantum machine learning in the field of healthcare. The study here emphasizes on a classification problem of a histopathological cancer detection using quantum transfer learning. Rather than using single transfer learning model, the work model presented here consists of multiple transfer learning models especially ResNet18, VGG-16, Inception-v3, AlexNet and several variational quantum circuits (VQC) with high expressibility. As a result, we provide a comparative analysis of the models and the best performing transfer learning model with the prediction AUC of approximately 93 percent for histopathological cancer detection. We also observed that for 1000 images with Resnet18, Hybrid Quantum and Classical (HQC) provided a slightly better accuracy of 88.5 percent than classical of 88.0 percent.

Related papers

• Analyzing Images of Blood Cells with Quantum Machine Learning Methods: Equilibrium Propagation and Variational Quantum Circuits to Detect Acute Myeloid Leukemia [0.0]
  This paper presents a feasibility study demonstrating that quantum machine learning (QML) algorithms achieve competitive performance on real-world medical imaging.<n>We evaluate an energy-based learning method that does not use backpropagation for automated detection of Acute Myeloid Leukemia (AML) from blood cell microscopy images.<n>Using limited subsets (50-250 samples per class) of the AML-Cytomorphology dataset (18,365 expert-annotated images), quantum methods achieve performance only 12-15% below classical CNNs.
  arXiv  Detail & Related papers  (2026-01-26T17:36:19Z)
• CQ CNN: A Hybrid Classical Quantum Convolutional Neural Network for Alzheimer's Disease Detection Using Diffusion Generated and U Net Segmented 3D MRI [0.0]
  The detection of Alzheimer disease (AD) from clinical MRI data is an active area of research in medical imaging. Recent advances in quantum computing offer new opportunities to develop models that may outperform traditional methods. We propose an end to end hybrid classical quantum convolutional neural network (CQ CNN) for AD detection using clinically formatted 3D MRI data.
  arXiv  Detail & Related papers  (2025-03-04T07:08:47Z)
• 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)
• An ensemble framework approach of hybrid Quantum convolutional neural networks for classification of breast cancer images [2.1659912179830023]
  Quantum neural networks are deemed suitable to replace classical neural networks in their ability to learn and scale up network models. Medical image classification pertains well to applications in deep learning, particularly, convolutional neural networks.
  arXiv  Detail & Related papers  (2024-09-24T10:43:27Z)
• Comparative Analysis of Transfer Learning Models for Breast Cancer Classification [10.677937909900486]
  This study investigates the efficiency of deep learning models in distinguishing between Invasive Ductal Carcinoma (IDC) and non-IDC in histopathology slides. We conducted a comparison examination of eight sophisticated models: ResNet-50, DenseNet-121, ResNeXt-50, Vision Transformer (ViT), GoogLeNet (Inception v3), EfficientNet, MobileNet, and SqueezeNet.
  arXiv  Detail & Related papers  (2024-08-29T18:49:32Z)
• Adversarial attacks on hybrid classical-quantum Deep Learning models for Histopathological Cancer Detection [0.0]
  The study emphasizes two primary applications of hybrid classical-quantum Deep Learning models. We compare the performance accuracy of the classical model with the hybrid classical-quantum model using pennylane default quantum simulator.
  arXiv  Detail & Related papers  (2023-09-08T06:37:54Z)
• Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network [63.845552349914186]
  Capturing global contextual information plays a critical role in breast ultrasound (BUS) image classification. Vision Transformers have an improved capability of capturing global contextual information but may distort the local image patterns due to the tokenization operations. In this study, we proposed a hybrid multitask deep neural network called Hybrid-MT-ESTAN, designed to perform BUS tumor classification and segmentation.
  arXiv  Detail & Related papers  (2023-08-04T01:19:32Z)
• Implementing a Hybrid Quantum-Classical Neural Network by Utilizing a Variational Quantum Circuit for Detection of Dementia [0.0]
  Nearly 1 in 3 patients with Alzheimer's were misdiagnosed in 2019, an issue neural networks can rectify. This study found that the proposed hybrid quantum-classical convolutional neural network (QCCNN) provided 97.5% and 95.1% testing and validation accuracies. QCCNN detected normal and demented images correctly 95% and 98% of the time, compared to the CNN accuracies of 89% and 91%.
  arXiv  Detail & Related papers  (2023-01-29T18:05:42Z)
• 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)
• 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)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• 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)

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.