[HTML][HTML] Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges
T Saba - Journal of infection and public health, 2020 - Elsevier
Cancer is a fatal illness often caused by genetic disorder aggregation and a variety of
pathological changes. Cancerous cells are abnormal areas often growing in any part of …
pathological changes. Cancerous cells are abnormal areas often growing in any part of …
A systematic review on recent advancements in deep and machine learning based detection and classification of acute lymphoblastic leukemia
Automatic Leukemia or blood cancer detection is a challenging job and is very much
required in healthcare centers. It has a significant role in early diagnosis and treatment …
required in healthcare centers. It has a significant role in early diagnosis and treatment …
A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches
Microorganisms such as bacteria and fungi play essential roles in many application fields,
like biotechnique, medical technique and industrial domain. Microorganism counting …
like biotechnique, medical technique and industrial domain. Microorganism counting …
Recognition of peripheral blood cell images using convolutional neural networks
Background and objectives Morphological analysis is the starting point for the diagnostic
approach of more than 80% of hematological diseases. However, the morphological …
approach of more than 80% of hematological diseases. However, the morphological …
A deep learning model (ALNet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images
L Boldú, A Merino, A Acevedo, A Molina… - Computer Methods and …, 2021 - Elsevier
Background and objectives Morphological differentiation among blasts circulating in blood
in acute leukaemia is challenging. Artificial intelligence decision support systems hold …
in acute leukaemia is challenging. Artificial intelligence decision support systems hold …
Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks
C Matek, S Schwarz, K Spiekermann… - Nature Machine …, 2019 - nature.com
Reliable recognition of malignant white blood cells is a key step in the diagnosis of
haematologic malignancies such as acute myeloid leukaemia. Microscopic morphological …
haematologic malignancies such as acute myeloid leukaemia. Microscopic morphological …
White blood cells identification system based on convolutional deep neural learning networks
Background and objectives White blood cells (WBCs) differential counting yields valued
information about human health and disease. The current developed automated cell …
information about human health and disease. The current developed automated cell …
Deep learning approach to peripheral leukocyte recognition
Q Wang, S Bi, M Sun, Y Wang, D Wang, S Yang - PloS one, 2019 - journals.plos.org
Microscopic examination of peripheral blood plays an important role in the field of diagnosis
and control of major diseases. Peripheral leukocyte recognition by manual requires medical …
and control of major diseases. Peripheral leukocyte recognition by manual requires medical …
An automatic nucleus segmentation and CNN model based classification method of white blood cell
White blood cells (WBCs) play a remarkable role in the human immune system. To diagnose
blood-related diseases, pathologists need to consider the characteristics of WBC. The …
blood-related diseases, pathologists need to consider the characteristics of WBC. The …
Segmentation of white blood cells and comparison of cell morphology by linear and naïve Bayes classifiers
J Prinyakupt, C Pluempitiwiriyawej - Biomedical engineering online, 2015 - Springer
Background Blood smear microscopic images are routinely investigated by haematologists
to diagnose most blood diseases. However, the task is quite tedious and time consuming …
to diagnose most blood diseases. However, the task is quite tedious and time consuming …