A system on chip for melanoma detection using FPGA-based SVM classifier

• URL: http://arxiv.org/abs/2109.14840v1
• Date: Thu, 30 Sep 2021 04:30:54 GMT
• Title: A system on chip for melanoma detection using FPGA-based SVM classifier
• Authors: Shereen Afifi, Hamid GholamHosseini, and Roopak Sinha
• Abstract summary: Support Vector Machine (SVM) is a robust machine learning model that shows high accuracy with different classification problems. We propose a hardware/software co-design for implementing the SVM on FPGA to realize melanoma detection on a chip.
• Score: 0.7646713951724011
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Support Vector Machine (SVM) is a robust machine learning model that shows high accuracy with different classification problems, and is widely used for various embedded applications. However , implementation of embedded SVM classifiers is challenging, due to the inherent complicated computations required. This motivates implementing the SVM on hardware platforms for achieving high performance computing at low cost and power consumption. Melanoma is the most aggressive form of skin cancer that increases the mortality rate. We aim to develop an optimized embedded SVM classifier dedicated for a low-cost handheld device for early detection of melanoma at the primary healthcare. In this paper, we propose a hardware/software co-design for implementing the SVM classifier onto FPGA to realize melanoma detection on a chip. The implemented SVM on a recent hybrid FPGA (Zynq) platform utilizing the modern UltraFast High-Level Synthesis design methodology achieves efficient melanoma classification on chip. The hardware implementation results demonstrate classification accuracy of 97.9%, and a significant hardware acceleration rate of 21 with only 3% resources utilization and 1.69W for power consumption. These results show that the implemented system on chip meets crucial embedded system constraints of high performance and low resources utilization, power consumption, and cost, while achieving efficient classification with high classification accuracy.

Related papers

• Q2SAR: A Quantum Multiple Kernel Learning Approach for Drug Discovery [39.58317527488534]
  This research demonstrates the successful application of a Quantum Multiple Kernel Learning framework to enhance QSAR classification.<n>We apply this methodology to a dataset for identifying DYRK1A kinase inhibitors.<n>By benchmarking the QMKL-SVM against a classical Gradient Boosting model, we show that the quantum-enhanced approach achieves a superior AUC score.
  arXiv  Detail & Related papers  (2025-06-17T19:00:47Z)
• A PyTorch-Compatible Spike Encoding Framework for Energy-Efficient Neuromorphic Applications [36.52207669429316]
  Spiking Neural Networks (SNNs) offer promising energy efficiency advantages, particularly when processing sparse spike trains. This paper introduces a novel, open-source PyTorch-compatible Python framework for spike encoding.
  arXiv  Detail & Related papers  (2025-04-15T09:50:03Z)
• GS-TransUNet: Integrated 2D Gaussian Splatting and Transformer UNet for Accurate Skin Lesion Analysis [44.99833362998488]
  We present a novel approach that combines 2D Gaussian splatting with the Transformer UNet architecture for automated skin cancer diagnosis. Our findings illustrate significant advancements in the precision of segmentation and classification. This integration sets new benchmarks in the field and highlights the potential for further research into multi-task medical image analysis methodologies.
  arXiv  Detail & Related papers  (2025-02-23T23:28:47Z)
• Machine Learning Enhanced Quantum State Tomography on FPGA [0.12171407135748794]
  Machine learning techniques have opened new avenues for real-time quantum state tomography (QST) We demonstrate the deployment of machine learning-based QST onto edge devices, specifically utilizing field programmable gate arrays (FPGAs)
  arXiv  Detail & Related papers  (2025-01-08T07:59:40Z)
• Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI [58.809276442508256]
  We propose a hybrid network via the combination of convolution neural network (CNN) and transformer layers. The experimental results on private and public DCE-MRI datasets demonstrate that the proposed hybrid network superior performance than the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-11T15:46:00Z)
• Hybrid Deep Learning Framework for Enhanced Melanoma Detection [3.004788114489393]
  The primary objective of our study is to enhance the accuracy and efficiency of melanoma detection through an innovative hybrid approach. We utilize the HAM10000 dataset to meticulously train the U-Net model, enabling it to precisely segment cancerous regions. We employ the ISIC 2020 dataset to train the EfficientNet model, optimizing it for the binary classification of skin cancer.
  arXiv  Detail & Related papers  (2024-07-16T04:58:47Z)
• Quantum Machine Learning with Application to Progressive Supranuclear Palsy Network Classification [0.0]
  We present a quantum machine learning model for the diagnosis of Progressive Supranuclear Palsy (PSP) disorder. The results suggest that quantum machine learning has led to noticeable advancement and outperforms classical frameworks. In particular, we have demonstrated the successful application of the present model on both a quantum simulator and real chips of the IBM quantum platform.
  arXiv  Detail & Related papers  (2024-07-06T14:16:31Z)
• Quantum Support Vector Machine for Prostate Cancer Detection: A Performance Analysis [0.0]
  We introduce the application of Quantum Support Vector Machine (QSVM) to this critical healthcare challenge. Our study outlines the remarkable improvements in diagnostic performance made by QSVM over the classic SVM technique. The findings reveal not only a comparable accuracy with classical SVM but also a $7.14%$ increase in sensitivity and a notably high F1-Score ($93.33%$)
  arXiv  Detail & Related papers  (2024-03-12T17:46:38Z)
• SLP-Net:An efficient lightweight network for segmentation of skin lesions [9.812172372998358]
  SLP-Net is an ultra-lightweight skin lesion segmentation network based on the spiking neural P(SNP) systems type mechanism. We design a lightweight multi-scale feature extractor without the usual encoder-decoder structure. Experiments at the ISIC2018 challenge demonstrate that the proposed model has the highest Acc and DSC among the state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-12-20T06:22:21Z)
• EKGNet: A 10.96{\mu}W Fully Analog Neural Network for Intra-Patient Arrhythmia Classification [79.7946379395238]
  We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification. We propose EKGNet, a hardware-efficient and fully analog arrhythmia classification architecture that archives high accuracy with low power consumption.
  arXiv  Detail & Related papers  (2023-10-24T02:37:49Z)
• First steps towards quantum machine learning applied to the classification of event-related potentials [68.8204255655161]
  Low information transfer rate is a major bottleneck for brain-computer interfaces based on non-invasive electroencephalography (EEG) for clinical applications. In this study, we investigate the performance of quantum-enhanced support vector classifier (QSVC) Training (predicting) balanced accuracy of QSVC was 83.17 (50.25) %.
  arXiv  Detail & Related papers  (2023-02-06T09:43:25Z)
• FPGA-optimized Hardware acceleration for Spiking Neural Networks [69.49429223251178]
  This work presents the development of a hardware accelerator for an SNN, with off-line training, applied to an image recognition task. The design targets a Xilinx Artix-7 FPGA, using in total around the 40% of the available hardware resources. It reduces the classification time by three orders of magnitude, with a small 4.5% impact on the accuracy, if compared to its software, full precision counterpart.
  arXiv  Detail & Related papers  (2022-01-18T13:59:22Z)
• 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)
• Dynamic hardware system for cascade SVM classification of melanoma [0.8594140167290097]
  Melanoma is the most dangerous form of skin cancer, which is responsible for the majority of skin cancer-related deaths. We aim to develop a handheld device featured with low cost and high performance to enhance early detection of melanoma at the primary healthcare.
  arXiv  Detail & Related papers  (2021-12-10T03:56:35Z)
• 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)

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.