DeepLeukNet—A CNN based microscopy adaptation model for acute lymphoblastic leukemia classification

Saeed, Umair; Kumar, Kamlesh; Khuhro, Mansoor Ahmed; Laghari, Asif Ali; Shaikh, Aftab Ahmed; Rai, Athaul

doi:10.1007/s11042-023-16191-2

Cited by 7 publications

(2 citation statements)

References 64 publications

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“…But it's crucial to remember that when using this method on small medical imaging datasets, overfitting may result. Saeed et al [18] use a CNN technology to offer an automated solution for identifying Leukaemia with acute lymph illness. Simulations used Acute Lymphoblastic Leukaemia-IDB 1 and Leukaemia-lb 2.…”

Section: Related Workmentioning

confidence: 99%

ODRNN: optimized deep recurrent neural networks for automatic detection of leukaemia

Shree,

Logeswari

2024

SIViP

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Leukaemia, a kind of cancer that may occur in individuals of all ages, including kids and adults, is a significant contributor to worldwide death rates. This illness is currently diagnosed by manual evaluation of blood samples obtained using microscopic imaging, which is frequently slower, lengthy, imprecise. Additionally, inspection under a microscope, leukemic cells look and develop similarly to normal cells, making identification more difficult. Convolutional Neural Networks (CNN) for Deep Learning has provided cutting-edge techniques for picture classification challenges throughout the previous several decades, there is still potential for development with regard to performance, effectiveness, and learning technique. As a consequence, the study provided a unique deep learning approach known as Optimized Deep Recurrent Neural Network (ODRNN) for identifying Leukaemia sickness by analysing microscopic images of blood samples. Deep recurrent neural networks (DRNN) are used in the recommended strategy for diagnosing Leukaemia, then the Red Deer Optimization algorithm (RDOA) applies to optimize the weight gained by DRNN. The mass of DRNN from RDOA will be tuned on the deer roaring rate behavior. The model that has been proposed is evaluated on two openly accessible Leukaemia blood sample datasets, AML, ALL_IDB1 and ALL_IDB2. It is possible to create an accurate computer-aided diagnosis for Leukaemia malignancy by using the proposed deep learning model, which shows encouraging results. The research work uses statistical metrics related to disease including specificity, recall, accuracy, precision and F1 score to assess the effectiveness of the proposed model for identification and classification. The proposed method achieves highly impressive results, with scores of 98.96%, 99.85%, 99.98%, 99.23%, and 99.98%, respectively.

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Section: Related Workmentioning

confidence: 99%

ODRNN: optimized deep recurrent neural networks for automatic detection of leukaemia

Shree,

Logeswari

2024

SIViP

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“…Following the convolutional layers, we introduced a max-pooling layer to use the feature map then reduces the most significant features into smaller patches. This technique is applied to all convolutional layers specified in the architecture [33], [34], [35]. The outcome of the final MaxPooling layer is pass by ta MaxAveragePooling layer, transforming it into a vector.…”

Section: Model Creationmentioning

confidence: 99%

Enhancing potato crop yield with AI-powered CNN-based leaf disease detection and tracking

Iftikhar,

Kandhro,

Kehar

et al. 2024

Mehran Univ. res. j. eng. technol.

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While plant diseases continue to have a severe impact on food production, farmers face a formidable challenge in trying to meet the escalating demands of a population that is expanding quickly for agricultural items like potatoes. Despite spending billions on disease management, farmers frequently struggle to effectively control disease without the aid of cutting-edge technology. The paper examines a disease diagnosis method based on deep learning. To be more precise, it uses a Convolutional Neural Network (CNN) method for the disease's detection and classification. This study examines the impact of data augmentation while conducting an extensive performance evaluation of the hyper-parameter in the setting of detecting plant diseases with a focus on potatoes. The experimental findings demonstrate the effectiveness of the suggested model's 98% accuracy. Considering growing global issues, this research aims to open new pathways for more efficient plant disease management and, eventually, increase agricultural output.

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Robust and high capacity image steganography technique using spiral-walk inter-block DCT coefficient differencing

Kapoor,

Shivani

2024

Multimed Tools Appl

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DeepLeukNet—A CNN based microscopy adaptation model for acute lymphoblastic leukemia classification

Cited by 7 publications

References 64 publications

ODRNN: optimized deep recurrent neural networks for automatic detection of leukaemia

ODRNN: optimized deep recurrent neural networks for automatic detection of leukaemia

Enhancing potato crop yield with AI-powered CNN-based leaf disease detection and tracking

Robust and high capacity image steganography technique using spiral-walk inter-block DCT coefficient differencing

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