Laparoscopic management of uncommon benign uterine tumors: a systematic review

Sleiman, Zaki; Hussein, Sara; Mohsen, Amani; Khazzaka, Aline; Tropea, Alessandro; Biondi, Antonio

doi:10.1007/s13304-019-00651-2

Cited by 1 publication

(1 citation statement)

References 46 publications

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“…The author of this research describes a model for detecting acute lymphocytic leukemia with separate depthwise convolutions (Clinton et al, 2020 ; Chatila et al, 2021 ). Diagnosing these tumor tissues as soon as possible is necessary to reduce the physical strain on the patient and the treatment difficulties that the disease brings (Sleiman et al, 2019 ; Aoun et al, 2020 ; Ali et al, 2022 ; Rizwan et al, 2022 ). Convolutional neural network (CNN) is tested using the transfer learning method.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient computer vision-based approach for acute lymphoblastic leukemia prediction

Almadhor

Sattar

Hejaili

et al. 2022

Front. Comput. Neurosci.

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Leukemia (blood cancer) diseases arise when the number of White blood cells (WBCs) is imbalanced in the human body. When the bone marrow produces many immature WBCs that kill healthy cells, acute lymphocytic leukemia (ALL) impacts people of all ages. Thus, timely predicting this disease can increase the chance of survival, and the patient can get his therapy early. Manual prediction is very expensive and time-consuming. Therefore, automated prediction techniques are essential. In this research, we propose an ensemble automated prediction approach that uses four machine learning algorithms K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB). The C-NMC leukemia dataset is used from the Kaggle repository to predict leukemia. Dataset is divided into two classes cancer and healthy cells. We perform data preprocessing steps, such as the first images being cropped using minimum and maximum points. Feature extraction is performed to extract the feature using pre-trained Convolutional Neural Network-based Deep Neural Network (DNN) architectures (VGG19, ResNet50, or ResNet101). Data scaling is performed by using the MinMaxScaler normalization technique. Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and Random Forest (RF) as feature Selection techniques. Classification machine learning algorithms and ensemble voting are applied to selected features. Results reveal that SVM with 90.0% accuracy outperforms compared to other algorithms.

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