Automated Acute Lymphocytic Leukemia (ALL) Detection Using Microscopic Images: An Efficient CAD Approach

Sumi, Tahmina Akter; Hossain, Mohammad Shahadat; Andersson, Karl

doi:10.1007/978-981-16-8826-3_31

Cited by 5 publications

(3 citation statements)

References 38 publications

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“…Recently, Sahlol et al [181] proposed a wrapper FS method termed as statistically enhanced Salp Swarm Algorithm (SESSA), where SVM was used as a classifier, to select deep features, extracted using VGG architecture, for the classification of pre-segmented white blood cells. Recently, Sumi et al [213] proposed a self-made CNN-architecture to detect presence of Leukemia. To train the model, they generated synthetic samples through a data augmentation process.…”

Section: Pathological Image Based Methodsmentioning

confidence: 99%

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Malakar¹,

Roy

Das

et al. 2022

Arch Computat Methods Eng

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Disease prediction from diagnostic reports and pathological images using artificial intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in recent days. Researchers are striving to achieve near-perfect results using advanced hardware technologies in amalgamation with AI and ML based approaches. As a result, a large number of AI and ML based methods are found in the literature. A systematic survey describing the state-of-the-art disease prediction methods, specifically chronic disease prediction algorithms, will provide a clear idea about the recent models developed in this field. This will also help the researchers to identify the research gaps present there. To this end, this paper looks over the approaches in the literature designed for predicting chronic diseases like Breast Cancer, Lung Cancer, Leukemia, Heart Disease, Diabetes, Chronic Kidney Disease and Liver Disease. The advantages and disadvantages of various techniques are thoroughly explained. This paper also presents a detailed performance comparison of different methods. Finally, it concludes the survey by highlighting some future research directions in this field that can be addressed through the forthcoming research attempts.

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Section: Pathological Image Based Methodsmentioning

confidence: 99%

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Malakar¹,

Roy

Das

et al. 2022

Arch Computat Methods Eng

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“…8 Making the diagnosis of leukemia using microscopic images and determining it without depending on expert experience represents an important situation for hematologists around the world. 9 Many technological devices such as X-ray, Magnetic Resonance Imaging (MRI), and Complete Blood Count (CBC) machines have helped diagnose diseases and future expectations of patients' conditions. 10,11 In the literature, studies based on image processing have been performed for detecting of leukemia cells and segmentation of WBC.…”

Section: Introductionmentioning

confidence: 99%

“…Researchers have been able to automate medical image testing thanks to recent advances in computer vision but applying these systems in medical image analysis presents significant challenges 8 . Making the diagnosis of leukemia using microscopic images and determining it without depending on expert experience represents an important situation for hematologists around the world 9 …”

Section: Introductionmentioning

confidence: 99%

Examining the classification performance of pre‐trained capsule networks on imbalanced bone marrow cell dataset

Aydin Atasoy,

Faris Abdulla Al Rahhawi

2024

Int J Imaging Syst Tech

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The automatic detection of bone marrow (BM) cell diseases plays a vital role in the medical field; it helps to make diagnoses more precise and effective, which leads to early detection and can significantly improve patient outcomes and increase the chances of successful intervention. This study proposed a fully automated intelligent system for BM classification by developing and enhancing Capsule Neural Network (CapsNet) architecture. Although CapsNet has demonstrated success in many classification tasks, it still has some limitations and challenges associated with using Convolutional Neural Networks (CNNs), which suffer from information loss during the pooling and discarding of detailed spatial information, resulting in the loss of fine‐grained features. Additionally, CNNs must help capture hierarchical feature relationships and often learn them implicitly by stacking convolutional layers. In contrast, CapsNets are designed to capture hierarchical features through dynamic routing and relationships between capsules, resulting in a more explicit representation of spatial hierarchy. CapsNets manage transformations and offer equivariance, preserving spatial information through capsule routing mechanisms. Further, to improve how features are represented, pre‐trained models such as Residual Capsule Network (RES‐CapsNet), Visual Geometry Group Capsule Network (VGG‐CapsNet), and Google Network (Inception V3) (GN‐CapsNet) have been used. This helps the network obtain the low‐ and mid‐level features and information it has previously learned so that subsequent capsule layers receive better initial information. Additionally, the Synthetic Minority Over‐Sampling Technique (SMOTE) was implemented to mitigate class imbalance. It generates synthetic samples in feature space by over‐sampling the minority class, leading to improving model performance in accurately classifying rare instances. Fine‐tuning the hyperparameters and implementing these improvements resulted in remarkable accuracy rates on a large BM dataset, with reduced training time and trainable parameters. CapsNet achieved 96.99%, VGG‐CapsNet achieved 98.95%, RES‐CapsNet achieved 99.24%, and the GN‐CapsNet model demonstrated superior accuracy at 99.45%. GN‐Caps Net was the best because it requires a small number of epochs and has an effective deep inception architecture that efficiently extracts features at different scales to form a robust representation of the input. Our proposed models were compared with existing state‐of‐the‐art models using the BM dataset; the results showed that our models outperformed the existing approaches and demonstrated excellent performance. Further, this automated system can analyze large amounts of data and complex cells in images of the BM dataset. Thus, it gives healthcare professionals a detailed understanding of different diseases, which may take time to achieve manually.

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Classifying Brain Tumor from MRI Images Using Parallel CNN Model

Sumi

Nath

Nahar

et al. 2022

Lecture Notes in Computer Science

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Automated Acute Lymphocytic Leukemia (ALL) Detection Using Microscopic Images: An Efficient CAD Approach

Cited by 5 publications

References 38 publications

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Examining the classification performance of pre‐trained capsule networks on imbalanced bone marrow cell dataset

Classifying Brain Tumor from MRI Images Using Parallel CNN Model

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