Amniotic fluid segmentation based on pixel classification using local window information and distance angle pixel

Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities

A fetal ultrasound (US) is a technique to examine a baby’s maturity and development. US examinations have varying purposes throughout pregnancy. Consequently, in the second and third trimester, US tests are performed for the assessment of Amniotic Fluid Volume (AFV), a key indicator of fetal health. Disorders resulting from abnormal AFV levels, commonly referred to as oligohydramnios or polyhydramnios, may pose a serious threat to a mother’s or child’s health. This paper attempts to accumulate and compare the most recent advancements in Artificial Intelligence (AI)-based techniques for the diagnosis and classification of AFV levels. Additionally, we provide a thorough and highly inclusive breakdown of other relevant factors that may cause abnormal AFV levels, including, but not limited to, abnormalities in the placenta, kidneys, or central nervous system, as well as other contributors, such as preterm birth or twin-to-twin transfusion syndrome. Furthermore, we bring forth a concise overview of all the Machine Learning (ML) and Deep Learning (DL) techniques, along with the datasets supplied by various researchers. This study also provides a brief rundown of the challenges and opportunities encountered in this field, along with prospective research directions and promising angles to further explore.

Machine learning model for umbilical cord classification using combination coiling index and texture feature based on 2-D Doppler ultrasound images

The umbilical cord is an organ that circulates oxygen and nutrition from mother to fetus during pregnancy. This study aims to classify the umbilical cord based on ultrasound images. The similarity of shape and coil between each class becomes a challenge. Therefore, it requires feature values that are relevant to the characteristics of these three classes. The condition of imbalanced data sets in this study is also an obstacle that causes the classifier’s performance to degrade on minority classes. Therefore, this study proposes a machine learning model capable of properly dealing with imbalanced data sets and recognizing the umbilical cord class.

Furthermore, this study proposes a new feature extraction method, namely, the umbilical coiling index (UCI), which directly adopts obstetricians’ knowledge. The proposed model consists of five stages: image preprocessing, feature extraction, feature selection, oversampling data using SMOTE, and Classification. Machine learning method observations were carried out comprehensively on five based classifiers: Random Forest, KNN, Decision tree, SVM, Naïve Bayes, and Multiclassifier. The results showed that the Random forest and Multiclassifier methods provide the highest accuracy, precision, recall, and F-measure performance in imbalanced data sets.

Breast microscopic cancer segmentation and classification using unique 4-qubit-quantum model

The visual inspection of histopathological samples is the benchmark for detecting breast cancer, but a strenuous and complicated process takes a long time of the pathologist practice. Deep learning models have shown excellent outcomes in clinical diagnosis and image processing and advances in various fields, including drug development, frequency simulation, and optimization techniques. However, the resemblance of histopathologic images of breast cancer and the inclusion of stable and infected tissues in different areas make detecting and classifying tumors on entire slide images more difficult. In breast cancer, a correct diagnosis is needed for complete care in a limited amount of time. An effective detection can relieve the pathologist's workload and mitigate diagnostic subjectivity. Therefore, this research work investigates improved the pre-trained xception and deeplabv3+ design semantic model. The model has been trained on input images with ground masks on the tuned parameters that significantly improve the segmentation of ultrasound breast images into respective classes, that is, benign/malignant. The segmentation model delivered an accuracy of greater than 99% to prove the model's effectiveness. The segmented images and histopathological breast images are transferred to the 4-qubit-quantum circuit with six-layered architecture to detect breast malignancy. The proposed framework achieved remarkable performance as contrasted to currently published methodologies. HIGHLIGHTS: This research proposed hybrid semantic model using pre-trained xception and deeplabv3 for breast microscopic cancer classification in to benign and malignant classes at accuracy of 95% accuracy, 99% accuracy for detection of breast malignancy.