Survey of Supervised Learning for Medical Image Processing

Aljuaid, Abeer; Anwar, Mohd

doi:10.1007/s42979-022-01166-1

Cited by 26 publications

(11 citation statements)

References 90 publications

(84 reference statements)

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“…The classification task is crucial in various fields, including medical imaging, where an accurate and efficient diagnosis is essential [Zhou et al 2021, Nazir et al 2023]. Traditional classifiers have been widely used for their interpretability and ability to handle high-dimensional data [Aljuaid and Anwar 2022]. In this study, we utilized two wellestablished classifiers: SVM and Naive Bayes.…”

Section: Traditional Classifiersmentioning

confidence: 99%

Improving Sickle Cell Disease Classification: A Fusion of Conventional Classifiers, Segmented Images, and Convolutional Neural Networks

Cardoso,

Moreira,

Mari

et al. 2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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Sickle cell anemia, which is characterized by abnormal erythrocyte morphology, can be detected using microscopic images. Computational techniques in medicine enhance the diagnosis and treatment efficiency. However, many computational techniques, particularly those based on Convolutional Neural Networks (CNNs), require high resources and time for training, highlighting the research opportunities in methods with low computational overhead. In this paper, we propose a novel approach combining conventional classifiers, segmented images, and CNNs for the automated classification of sickle cell disease. We evaluated the impact of segmented images on classification, providing insight into deep learning integration. Our results demonstrate that using segmented images and CNN features with an SVM achieves an accuracy of 96.80%. This finding is relevant for computationally efficient scenarios, paving the way for future research and advancements in medical-image analysis.

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Section: Traditional Classifiersmentioning

confidence: 99%

Improving Sickle Cell Disease Classification: A Fusion of Conventional Classifiers, Segmented Images, and Convolutional Neural Networks

Cardoso,

Moreira,

Mari

et al. 2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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“…The motivation for extracting features of the IHC images with pretrained CNNs and constructing FRPs of these extracted features is based on the following aspects of data analysis for machine learning. Firstly, extracting deep features of new data from pre-trained CNNs has been reported very useful in terms of robustness and computational advantage for the classification of complex biomedical images [20] , [21] , [22] , [23] . Secondly, the transformation of flattened deep image features into FRPs is expected to enhance the power of machine learning as the spatial-temporal content of the original IHC data can be captured by this method of nonlinear dynamics.…”

Section: Introductionmentioning

confidence: 99%

Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients

Pham

Ravi

Fan

et al. 2023

IEEE J. Transl. Eng. Health Med.

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Background: Over a decade, tissues dissected adjacent to primary tumors have been considered ''normal'' or healthy samples (NATs). However, NATs have recently been discovered to be distinct from both tumorous and normal tissues. The ability to predict the survival rate of cancer patients using NATs can open a new door to selecting optimal treatments for cancer and discovering biomarkers. Methods: This paper introduces an artificial intelligence (AI) approach that uses NATs for predicting the 5-year survival of pre-operative radiotherapy patients with rectal cancer. The new approach combines pre-trained deep learning, nonlinear dynamics, and long short-term memory to classify immunohistochemical images of RhoB protein expression on NATs. Results: Ten-fold cross-validation results show 88% accuracy of prediction obtained from the new approach, which is also higher than those provided from baseline methods. Conclusion: Preliminary results not only add objective evidence to recent findings of NATs' molecular characteristics using state-of-the-art AI methods, but also contribute to the discovery of RhoB expression on NATs in rectal-cancer patients. Clinical impact: The ability to predict the survival rate of cancer patients is extremely important for clinical decision-making. The proposed AI tool is promising for assisting oncologists in their treatments of rectal cancer patients.

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“…Supervised learning is a training method for neural networks widely used in the fields of pattern recognition (Schwenker and Trentin, 2014), image processing (Aljuaid and Anwar, 2022), and semantic segmentation (Zhou et al, 2022), which is generally realized on the graphics processing unit (GPU) and the central processing unit (CPU). Due to the frequent data transmission between memories and process units, the GPU and the CPU are difficult to solve problems, such as high energy consumption and high demand for hardware specifications.…”

Section: Introductionmentioning

confidence: 99%

Presynaptic spike-driven plasticity based on eligibility trace for on-chip learning system

Gao

Deng²,

Wang³

et al. 2023

Front. Neurosci.

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IntroductionRecurrent spiking neural network (RSNN) performs excellently in spatio-temporal learning with backpropagation through time (BPTT) algorithm. But the requirement of computation and memory in BPTT makes it hard to realize an on-chip learning system based on RSNN. In this paper, we aim to realize a high-efficient RSNN learning system on field programmable gate array (FPGA).MethodsA presynaptic spike-driven plasticity architecture based on eligibility trace is implemented to reduce the resource consumption. The RSNN with leaky integrate-and-fire (LIF) and adaptive LIF (ALIF) models is implemented on FPGA based on presynaptic spike-driven architecture. In this architecture, the eligibility trace gated by a learning signal is used to optimize synaptic weights without unfolding the network through time. When a presynaptic spike occurs, the eligibility trace is calculated based on its latest timestamp and drives synapses to update their weights. Only the latest timestamps of presynaptic spikes are required to be stored in buffers to calculate eligibility traces.ResultsWe show the implementation of this architecture on FPGA and test it with two experiments. With the presynaptic spike-driven architecture, the resource consumptions, including look-up tables (LUTs) and registers, and dynamic power consumption of synaptic modules in the on-chip learning system are greatly reduced. The experiment results and compilation results show that the buffer size of the on-chip learning system is reduced and the RSNNs implemented on FPGA exhibit high efficiency in resources and energy while accurately solving tasks.DiscussionThis study provides a solution to the problem of data congestion in the buffer of large-scale learning systems.

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Survey of Supervised Learning for Medical Image Processing

Cited by 26 publications

References 90 publications

Improving Sickle Cell Disease Classification: A Fusion of Conventional Classifiers, Segmented Images, and Convolutional Neural Networks

Improving Sickle Cell Disease Classification: A Fusion of Conventional Classifiers, Segmented Images, and Convolutional Neural Networks

Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients

Presynaptic spike-driven plasticity based on eligibility trace for on-chip learning system

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