Detection of brain tumors plays a vital role in medical image processing. If the brain tumors are detected in advance then there is a chance to improve the treatment options and boost the patient’s survival percentage. Brain tumor detection to identify cancer from a significant number of MRI images acquired in medical practice remains as a tedious and complicated task. As a result, there is a need to authomate the image processing based brain tumor detection process. This paper aims to propose an early brain tumor detection method by using a Convolutional Neural Network (CNN) architecture. The positive outcome illustrates the algorithm’s capacity to correctly classify brain tumor images. Performance metrics such as accuracy and F1 score are used to assess the performance of the proposed approach. When compared to conventional methods, the proposed method works efficiently.

Brain Tumor Detection using Convolution Neural Network with Data Augmentation

Detection of brain tumors plays a vital role in medical image processing. If the brain tumors are detected in advance then there is a chance to improve the treatment options and boost the patient’s survival percentage. Brain tumor detection to identify cancer from a significant number of MRI images acquired in medical practice remains as a tedious and complicated task. As a result, there is a need to authomate the image processing based brain tumor detection process. This paper aims to propose an early brain tumor detection method by using a Convolutional Neural Network (CNN) architecture. The positive outcome illustrates the algorithm’s capacity to correctly classify brain tumor images. Performance metrics such as accuracy and F1 score are used to assess the performance of the proposed approach. When compared to conventional methods, the proposed method works efficiently. 

Breast cancer is the deadliest and most common cancer in the world. Early treatment of this cancer can help to nip it in the bud. In present medical setting, this cancer is identified by manual clinical procedures, which can lead to human errors and further delay the treatment procedure. So, we propose a Convolutional Neural Network (CNN) model employed with transfer learning approach with RESNET50, VGG19 and InceptionV3 algorithms. The histopathological image dataset is used to detect cancer cells in the tissues of the breast. We examine the performance of different models based on their accuracy, by varying different optimizers (Adam, SGDM and RMSProp) for each transfer learning model. The results show that the Inception-V3 model with Adam optimizer outperforms VGG19 and RESNET-50 in terms of accuracy. 

Breast Cancer Classification using CNN with Transfer Learning Models

This study focused on an innovative practical method using computer vision for particle size measurement, which serves as a key precursor for predicting the elastic modulus of polymer nanocomposites. This approach involved the morphological segmentation of the nanodispersed phase. It aimed, for the first time, to address the impractical conditions resulting from the assumption of idealized single-particle sizes in a monodispersed system during modeling. Subsequently, a micromechanical finite element framework was employed to determine the interphase thickness and modulus in ultrahigh molecular weight polyethylene/nanozeolite composites, following the quantification of nanoparticle sizes. The size measurement approach relied on morphological images extracted from scanning electron microscopy micrographs of impact-fractured surfaces. To compute the interphase thickness, experimental data was fitted to an interphase-inclusive upper-bound Hashin-Shtrikman model, with the measured average particle size per composition serving as a crucial input. Subsequently, the interphase elastic modulus was computed based on its thickness, employing a hybrid modified-Hashin-Hansen and Maxwell model. These estimated interfacial variables were then utilized as inputs for the finite element model to determine the tensile modulus. A comparison between the model results and measured data revealed a maximum discrepancy of 3.29%, indicating the effectiveness of the methodology employed in quantifying interfacial properties. 

Morphology-Driven Nanofiller Size Measurement Integrated with Micromechanical Finite Element Analysis for Quantifying Interphase in Polymer Nanocomposites

This article presents Multi-modal MRI scans used to classify brain tumors according to their size and imaging appearance. Object detection has been significantly improved by utilizing convolutional neural networks and deep learning approaches, resulting in superior performance. Our solution to tackle uncertainty involves a new deep learning method that incorporates pre-processing techniques such as data augmentation, as well as a customized convolutional neural network. The proposed method aims to achieve three objectives. (1) To address overfitting concerns, pre-processing techniques such as data augmentation are employed. (2) A customized deep learning method is used, which includes a convolutional neural network, to classify brain tumors. (3) A Web APP is utilized to provide information related to the tumor. The suggested procedure's performance is evaluated using sensitivity, specificity, and accuracy, and it outperforms traditional deep learning methods. Additionally, the entire process can be completed on the Python platform. 

Detection Of MRI Brain Tumor Using Customized Deep Learning Method Via Web App

Brain tumor MRI image segmentation using an optimized multi-kernel FCM method with a pre-processing stage

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Brain tumor MRI image segmentation using an optimized multi-kernel FCM method with a pre-processing stage