Efficient computer‐aided diagnosis technique for leukaemia cancer detection

Abdulla, Alan Anwer

doi:10.1049/iet-ipr.2020.0978

Cited by 28 publications

(5 citation statements)

References 26 publications

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“…Digital image processing (DIP) is significant in many areas, particularly medical image processing, image in-painting, pattern recognition, biometrics, content-based image retrieval, image de-hazing, and multimedia security [1], [2]. It is becoming more important for analyzing medical images and identifying abnormalities in these images.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Diagnosis for the Early Breast Cancer Detection

Aziz¹,

Abdulla²

2023

UHD J SCI TECH

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Computer-aided diagnosis (CAD) system is a prominent tool for the detection of different forms of diseases, especially cancers, based on medical imaging. Digital image processing is a critical in the processing and analysis of medical images for the disease diagnosis and detection. This study introduces a CAD system for detecting breast cancer. Once the breast region is segmented from the mammograms image, certain texture and statistical features are extracted. GLRLM feature extraction technique is implemented to extracted texture features. On the other hand, statistical features such as skewness, mean, entropy, and standard deviation are extracted. Consequently, on the basis of the extracted features, SVM and kNN classifier techniques are utilized to classify the segmented region as normal or abnormal. The performance of the proposed approach has been investigated via extensive experiments conducted on the well-known MIAS dataset of mammography images. The experimental findings show that the suggested approach outperforms other existing approaches, with an accuracy rate of 99.7%.

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Section: Introductionmentioning

confidence: 99%

Computer-Aided Diagnosis for the Early Breast Cancer Detection

Aziz¹,

Abdulla²

2023

UHD J SCI TECH

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“…Computer-Aided Diagnosis (CAD) systems assume a pivotal role in the diagnosis and treatment of diseases, providing valuable assistance to healthcare professionals in the precise detection, characterization, and analysis of disease-related anomalies depicted in medical images [ 1 ]. By employing sophisticated algorithms and machine learning methodologies, CAD systems aim to bolster the efficiency and accuracy of disease diagnosis and treatment planning.…”

Section: Introductionmentioning

confidence: 99%

Deep fusion of multi-modal features for brain tumor image segmentation

Zhang,

Zhou,

et al. 2023

Heliyon

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“…Given the very large numbers of cytopathological images to be processed, diagnosis becomes cumbersome and time consuming for doctors, and diagnostic accuracy is also closely related to experts' experience, fatigue and mood and so on. In view of the facts many researchers have proposed some methods (4)(5)(6)(7)(8)(9)(10)(11)(12)(13) for diagnosis of leukemia. The critical step of which is segmentation.…”

Section: Introductionmentioning

confidence: 99%

A lightweight network based on dual-stream feature fusion and dual-domain attention for white blood cells segmentation

Luo,

Wang,

Zhao

et al. 2023

Front. Oncol.

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IntroductionAccurate white blood cells segmentation from cytopathological images is crucial for evaluating leukemia. However, segmentation is difficult in clinical practice. Given the very large numbers of cytopathological images to be processed, diagnosis becomes cumbersome and time consuming, and diagnostic accuracy is also closely related to experts' experience, fatigue and mood and so on. Besides, fully automatic white blood cells segmentation is challenging for several reasons. There exists cell deformation, blurred cell boundaries, and cell color differences, cells overlapping or adhesion.MethodsThe proposed method improves the feature representation capability of the network while reducing parameters and computational redundancy by utilizing the feature reuse of Ghost module to reconstruct a lightweight backbone network. Additionally, a dual-stream feature fusion network (DFFN) based on the feature pyramid network is designed to enhance detailed information acquisition. Furthermore, a dual-domain attention module (DDAM) is developed to extract global features from both frequency and spatial domains simultaneously, resulting in better cell segmentation performance.ResultsExperimental results on ALL-IDB and BCCD datasets demonstrate that our method outperforms existing instance segmentation networks such as Mask R-CNN, PointRend, MS R-CNN, SOLOv2, and YOLACT with an average precision (AP) of 87.41%, while significantly reducing parameters and computational cost.DiscussionOur method is significantly better than the current state-of-the-art single-stage methods in terms of both the number of parameters and FLOPs, and our method has the best performance among all compared methods. However, the performance of our method is still lower than the two-stage instance segmentation algorithms. in future work, how to design a more lightweight network model while ensuring a good accuracy will become an important problem.

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Efficient computer‐aided diagnosis technique for leukaemia cancer detection

Cited by 28 publications

References 26 publications

Computer-Aided Diagnosis for the Early Breast Cancer Detection

Computer-Aided Diagnosis for the Early Breast Cancer Detection

Deep fusion of multi-modal features for brain tumor image segmentation

A lightweight network based on dual-stream feature fusion and dual-domain attention for white blood cells segmentation

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