Breast Cancer Classification using Deep Learned Features Boosted with Handcrafted Features

Breast cancer is one of the leading causes of death among women across the globe. It is difficult to treat if detected at advanced stages, however, early detection can significantly increase chances of survival and improves lives of millions of women. Given the widespread prevalence of breast cancer, it is of utmost importance for the research community to come up with the framework for early detection, classification and diagnosis. Artificial intelligence research community in coordination with medical practitioners are developing such frameworks to automate the task of detection. With the surge in research activities coupled with availability of large datasets and enhanced computational powers, it expected that AI framework results will help even more clinicians in making correct predictions. In this article, a novel framework for classification of breast cancer using mammograms is proposed. The proposed framework combines robust features extracted from novel Convolutional Neural Network (CNN) features with handcrafted features including HOG (Histogram of Oriented Gradients) and LBP (Local Binary Pattern). The obtained results on CBIS-DDSM dataset exceed state of the art

Diagnostic Challenges in Prostate Cancer and 68Ga-PSMA PET Imaging: A Game Changer?

Creative Commons Attribution LicenseProstate cancer (PC) is the most frequent solid tumor in men and the third most common cause of cancer mortalityamong men in developed countries. Current imaging modalities like ultrasound (US), computerized tomography (CT),magnetic resonance imaging (MRI) and choline based positron emission (PET) tracing have disappointing sensitivity fordetection of nodal metastasis and small tumor recurrence. This poses a diagnostic challenge in staging of intermediateto high risk PC and restaging of patients with biochemical recurrence (PSA \u3e0.2 ng/ml). Gallium-68 labeled prostatespecific membrane antigen (68Ga-PSMA) PET imaging has now emerged with a higher diagnostic yield. 68Ga-PSMAPET/CT or PET/MRI can be expected to offer a one-stop-shop for staging and restaging of PC. PSMA ligands labeledwith alpha and beta emitters have also shown promising therapeutic efficacy for nodal, bone and visceral metastasis.Therefore a PSMA based theranostics approach for detection, staging, treatment, and follow-up of PC would appearto be highly valuable to achieve personalized PC treatment

Sentiment Analysis of Omicron Tweets by using Machine Learning Models

The COVID-19 epidemic has been affecting a lot of individuals worldwide since 2019. It is emerging as an infectious disease that set off a disaster with far-reaching effects on things like education, economics, and health. During the coronavirus outbreak, new COVID-19 mutations such the Beta, Delta, and Omicron variants emerged, terrifying and alarmed the population. Around 6 million people reportedly died as a result of COVID-19 variations, according to World Meter. The SARS-CoV-2 omicron strain was initially identified in South Africa on November 24, 2021, and it has since spread to more than 57 nations. In this essay, we examine how people feel and act toward the omicron variation. On Omicron, we proposed an approach for determining sentiment analysis for tweets from Twitter. The analysis of Twitter data's sentiment has a lot of potential. In the intended methodology, we extract the best characteristics from the Omicron tweets using NLP techniques in Python, resulting in a dataset that can be used to train the Models. The produced dataset was employed by four ML Classifiers, including “Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM)”, to accurately categorise users' emotional behavior into three categories: neutral, negative, and positive. The Class Neutral receives the best score and the Class Negative receives the lowest score based on the accuracy of the forecast level