Ultrasound super resolution imaging for accurate uterus tumor detection and malignancy prediction - Ashwini Sawant; Sujata Kulkarni; Milind Sawant (Journal of Pharmaceutical and Biomedical Analysis Open)
The term ‘tumor’ describes an atypical development of cells that forms a mass inside an organ; depending on the organ's invasive nature and propensity for metastasis, the growth may be benign or malignant. Improving patient outcomes requires the early detection of malignant tumors. Despite its lower resolution and noise problems, ultrasound, which is frequently used to diagnose uterine tumors, is safer and more economical than magnetic resonance imaging (MRI) scans and biopsies. Ultrasound pictures can be processed using methods including denoising, enhancement, segmentation, and feature extraction to get around these restrictions and boost their quality. Enhanced ultrasound images can reach even higher accuracy, cementing them as plausible alternatives to MRI. A comparative analysis of copious relevant image de-speckling, image enhancement, segmentation, and feature extraction methods are carried out. Higher resolution and superior quality, strong segmented real-time ultrasound uterus tumour images are produced by using diffusion-based hybrid filters, Super Resolution Convolutional Neural Networks (SRCNN), and U-net segmentation technique. The Grey Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are used to extract textural features. With the help of several machine learning approaches, such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Random Forest classifiers (RFC), the extracted characteristics are immediately sent to classifiers to classify uterus tumours from ultrasound images between benign and malignant. For the categorization of uterine tumors, the RFC classifier outperformed the other classifiers. The viability of cancer detection using ultrasound pictures is significantly strengthened by the suggested machine learning methodology. Multiple hospitals provided data on ultrasound pictures of uterine tumors, which were used to develop the model and obtain the prediction findings. A radiologist with 17 years of expertise in diagnostic radiology further assessed this dataset. We could produce high-quality ultrasonic real-time images of uterine tumor datasets with the suggested machine learning model at a 97.8 % accuracy rate utilizing RFC.