AIMC Topic: Leukemia

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Dynamic kernel generation through hybrid involution and convolution neural networks for leukemia and white blood cell classification.

Scientific reports
Blood cancer diagnosis through microscopic image analysis is challenging due to subtle morphological differences between cell stages and subtypes. This study aims to develop a Hybrid Involutional-Convolutional Neural Network (HICNN) for automated leu...

Association between exposure to PM and black carbon and the risk of childhood leukemia in Tehran: A case-control study with critical exposure time windows.

Environmental research
Limited research has explored the relationship between air pollutants and childhood leukemia during critical exposure periods, and no such research has been conducted in Tehran to date. This study assessed the association between exposure to fine par...

Prediction models for different types of leukemia: a systematic review and critical appraisal.

Journal of cancer research and clinical oncology
OBJECTIVES: To systematically review and evaluate the methodological quality and risk of bias (ROB) of leukemia prediction models essential for clinical decision-making.

Ocotillo optimization-driven deep learning for bone marrow cytology classification.

PloS one
Manual diagnosis of hematological cancers like leukemia through bone marrow smear analysis is labor-intensive, prone to errors, and highly dependent on expert knowledge. To overcome these limitations, this study introduces a comprehensive deep learni...

Multiclass leukemia cell classification using hybrid deep learning and machine learning with CNN-based feature extraction.

Scientific reports
Leukemia is the most prevalent form of blood cancer, affecting individuals across all age groups. Early and accurate diagnosis is crucial for effective treatment and improved clinical outcomes. Peripheral blood smear analysis, a key non-invasive diag...

Why implementing machine learning algorithms in the clinic is not a plug-and-play solution: a simulation study of a machine learning algorithm for acute leukaemia subtype diagnosis.

EBioMedicine
BACKGROUND: Artificial intelligence (AI) and machine learning (ML) algorithms have shown great promise in clinical medicine. Despite the increasing number of published algorithms, most remain unvalidated in real-world clinical settings. This study ai...

Diagnosis and typing of leukemia using a single peripheral blood cell through deep learning.

Cancer science
Leukemia is highly heterogeneous, meaning that different types of leukemia require different treatments and have different prognoses. Current clinical diagnostic and typing tests are complex and time-consuming. In particular, all of these tests rely ...

Combining array-assisted SERS microfluidic chips and machine learning algorithms for clinical leukemia phenotyping.

Talanta
The disease progression and treatment options of leukemia between different subtypes vary considerably, emphasizing the importance of phenotyping. However, early typing of leukemia remains challenging due to the lack of highly sensitive and specific ...

Leukemia detection and classification using computer-aided diagnosis system with falcon optimization algorithm and deep learning.

Scientific reports
Leukemia is a type of blood tumour that occurs because of abnormal enhancement in WBCs (white blood cells) in the bone marrow of the human body. Blood-forming tissue cancer influences the lymphatic and bone marrow system. The early diagnosis and dete...