Automated Brain Tumor Detection from MRI Scans using Deep Convolutional Neural Networks

The brain, as the central nervous system's most critical part, can develop abnormal growths of cells known as tumors. Cancer is the term used to describe malignant tumors. Medical imaging modalities, such as computed tomography (CT) or magnetic resonance imaging (MRI), are commonly used to detect cancerous regions in the brain. Other techniques, such as positron emission tomography (PET), cerebral arteriography, lumbar puncture, and molecular testing, are also utilized for brain tumor detection. MRI scans provide detailed information concerning delicate tissue, which aids in diagnosing brain tumors. MRI scan images are analyzed to assess the disease condition objectively. The proposed system aims to identify abnormal brain images from MRI scans accurately. The segmented mask can estimate the tumor's density, which is helpful in therapy. Deep learning techniques are employed to automatically extract features and detect abnormalities from MRI images. The proposed system utilizes a convolutional neural network (CNN), a popular deep learning technique, to analyze MRI images and identify abnormal brain scans with high accuracy. The system's training process involves feeding the CNN with large datasets of normal and abnormal MRI images to learn how to differentiate between the two. During testing, the system classifies MRI images as either normal or abnormal based on the learned features. The system's ability to accurately identify abnormal brain scans can aid medical practitioners in making informed decisions and providing better patient care. Additionally, the system's ability to estimate tumor density from the segmented mask provides additional information to guide therapy. The proposed system offers a promising solution for improving the accuracy and efficiency of brain tumor detection from MRI images, which is critical for early detection and treatment

A case report of caesarean myomectomy in a term pregnant woman with fibroid complicating pregnancy: in a tertiary care centre

Uterine leiomyomas are the benign tumors arising from the muscle cells of the uterus. These are the most common tumors observed in reproductive age group and is seen in around 2% pregnant women. The effect of fibroids on the pregnancy varies according to the number, size and their location. Most of the patients are asymptomatic. Recent literature suggests myomectomy during pregnancy and caesarean section is safe in well selected cases with experienced obstetrician in a tertiary care center

Artificial Neural Networks for Soil Quality and Crop Yield Prediction using Machine Learning

Agriculture is the main stream on which farmers depend. Many surveys have proved that suicide rate of farmers is proliferate over years. The main reasons for the increase in suicide rate are weather conditions, debts, lack of details about the soil. In some remote areas farmers lack information about soil quality, soil nutrients, soil composition and may choose wrong crop to sow which results in less yield. So as to overcome the issues faced by farmers we are trying to implement a model using Artificial Neural Networks (ANN) which predicts the soil quality taking input as several important parameters related to soil. This paper mainly focuses on predicting the crop yield using the ANN which is completely a software solution and also recommends suitable fertilizers to gain high yield of crops

An Enhanced Table Driven Source Routing Protocol for Wireless Ad Hoc Networks

Analysis of MANETs led to the research on network layer. Different routing protocols were designed for numerous objectives and purposes. The way data packets are handled with in a multi-hop wireless network refers to Opportunistic data forwarding. During present research, we propose enhanced table-driven source routing protocol. This protocol maintains additional topology information which is different from Distance Vector (DV) routing protocol. The proposed approach will reduce overhead compared to the ancient Distance Vector based protocols. Base on the test results performed using Computer Simulator (Network Simulator 2) observed that the overhead in the proposed solution is just a fraction of the overhead of the standard proactive protocols. Performance of the current solution is better for transportation of higher information compared to existing proactive routing protocols

DESIGN AND SIMULATION OF THREE PHASE FIVE LEVEL AND SEVEN LEVEL INVERTER FED INDUCTION MOTOR DRIVE WITH TWO CASCADED H-BRIDGE CONFIGURATION

This paper deals with study of Three phase Five Level and Seven Level inverter fed induction motor drive . Both five level and seven level are realized by cascading two H- bridges. The poor quality of voltage and current of a conventional inverter fed induction machine is due to the presence of harmonics and hence there is significant level of energy losses. The Multilevel inverter is used to reduce the harmonics. The inverters with a large number of steps can generate high quality voltage waveforms. The higher levels can follow a voltage reference with accuracy and with the advantage that the generated voltage can be modulated in amplitude instead of pulse-width modulation. An active harmonic elimination method is applied to eliminate any number of specific higher order harmonics of multilevel converters with unequal dc voltages. The simulation of three phase five and seven level inverter fed induction motor model is done using Matlab/Simulink. The FFT spectrums for the outputs are analyzed to study the reduction in the harmonics

An automated algorithm for the quantification of hCG level in novel fabric-based home pregnancy test kits

We report a new image processing algorithm that extracts quantitative information about the concentration of human chorionic gonadotropin (hCG), an important early pregnancy marker, from commercially available qualitative home pregnancy kits. The algorithm could potentially be ported onto a simple camera based cell phone making it a low-cost, portable point-of-care device as opposed to costly and time consuming clinical labs for accurate quantitative determination of hCG. The algorithm takes the image of the test result as input, classifies and determines the hCG concentration based on the RGB intensities of the test line. The efficacy of the algorithm is demonstrated using control samples on commercially available strips as well as novel fabric based strips designed for this application