Efficient Brain Tumor Detection Based on Deep Learning Models

Abstract Brain tumor is an acute cancerous disease that results from abnormal and uncontrollable cell division. Brain tumors are classified via biopsy, which is not normally done before the brain ultimate surgery. Recent advances and improvements in deep learning technology helped the health industry in getting accurate disease diagnosis. In this paper, a Convolutional Neural Network (CNN) is adopted with image pre-processing to classify brain Magnetic Resonance (MR) images into four classes: glioma tumor, meningioma tumor, pituitary tumor and normal patients, is provided. We use a transfer learning model, a CNN-based model that is designed from scratch, a pre-trained inceptionresnetv2 model and a pre-trained inceptionv3 model. The performance of the four proposed models is tested using evaluation metrics including accuracy, sensitivity, specificity, precision, F1_score, Matthew’s correlation coefficient, error, kappa and false positive rate. The obtained results show that the two proposed models are very effective in achieving accuracies of 93.15% and 91.24% for the transfer learning model and BRAIN-TUMOR-net based on CNN, respectively. The inceptionresnetv2 model achieves an accuracy of 86.80% and the inceptionv3 model achieves an accuracy of 85.34%. Practical implementation of the proposed models is presented.

Download Full-text

Brain Tumor Classification With Inception Network Based Deep Learning Model Using Transfer Learning

2020 IEEE Region 10 Symposium (TENSYMP) ◽

10.1109/tensymp50017.2020.9230618 ◽

2020 ◽

Author(s):

Mohd. Farhan Israk Soumik ◽

Md. Ali Hossain

Keyword(s):

Deep Learning ◽

Brain Tumor ◽

Transfer Learning ◽

Learning Model ◽

Tumor Classification ◽

Deep Learning Model

Download Full-text

Transfer Learning of a Deep Learning Model for Exploring Tourists’ Urban Image Using Geotagged Photos

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030137 ◽

2021 ◽

Vol 10 (3) ◽

pp. 137

Author(s):

Youngok Kang ◽

Nahye Cho ◽

Jiyoung Yoon ◽

Soyeon Park ◽

Jiyeon Kim

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Field Studies ◽

Learning Model ◽

Learning Technologies ◽

Regional Study ◽

Processing Technologies ◽

Final Model ◽

Urban Image ◽

Deep Learning Model

Recently, as computer vision and image processing technologies have rapidly advanced in the artificial intelligence (AI) field, deep learning technologies have been applied in the field of urban and regional study through transfer learning. In the tourism field, studies are emerging to analyze the tourists’ urban image by identifying the visual content of photos. However, previous studies have limitations in properly reflecting unique landscape, cultural characteristics, and traditional elements of the region that are prominent in tourism. With the purpose of going beyond these limitations of previous studies, we crawled 168,216 Flickr photos, created 75 scenes and 13 categories as a tourist’ photo classification by analyzing the characteristics of photos posted by tourists and developed a deep learning model by continuously re-training the Inception-v3 model. The final model shows high accuracy of 85.77% for the Top 1 and 95.69% for the Top 5. The final model was applied to the entire dataset to analyze the regions of attraction and the tourists’ urban image in Seoul. We found that tourists feel attracted to Seoul where the modern features such as skyscrapers and uniquely designed architectures and traditional features such as palaces and cultural elements are mixed together in the city. This work demonstrates a tourist photo classification suitable for local characteristics and the process of re-training a deep learning model to effectively classify a large volume of tourists’ photos.

Download Full-text

Transfer Learning of The ResNet-18 and DenseNet-121 Model Used to Diagnose Intracranial Hemorrhage in CT Scanning

Current Pharmaceutical Design ◽

10.2174/1381612827666211213143357 ◽

2021 ◽

Vol 27 ◽

Author(s):

Qi Zhou ◽

Wenjie Zhu ◽

Fuchen Li ◽

Mingqing Yuan ◽

Linfeng Zheng ◽

...

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Intracranial Hemorrhage ◽

Intraventricular Hemorrhage ◽

Learning Model ◽

Normal Group ◽

Subdural Hemorrhage ◽

Model Assessment ◽

Learning Models ◽

Deep Learning Model

Objective: To verify the ability of the deep learning model in identifying five subtypes and normal images in noncontrast enhancement CT of intracranial hemorrhage. Method: A total of 351 patients (39 patients in the normal group, 312 patients in the intracranial hemorrhage group) performed with intracranial hemorrhage noncontrast enhanced CT were selected, with 2768 images in total (514 images for the normal group, 398 images for the epidural hemorrhage group, 501 images for the subdural hemorrhage group, 497 images for the intraventricular hemorrhage group, 415 images for the cerebral parenchymal hemorrhage group, and 443 images for the subarachnoid hemorrhage group). Based on the diagnostic reports of two radiologists with more than 10 years of experience, the ResNet-18 and DenseNet-121 deep learning models were selected. Transfer learning was used. 80% of the data was used for training models, 10% was used for validating model performance against overfitting, and the last 10% was used for the final evaluation of the model. Assessment indicators included accuracy, sensitivity, specificity, and AUC values. Results: The overall accuracy of ResNet-18 and DenseNet-121 models were 89.64% and 82.5%, respectively. The sensitivity and specificity of identifying five subtypes and normal images were above 0.80. The sensitivity of DenseNet-121 model to recognize intraventricular hemorrhage and cerebral parenchymal hemorrhage was lower than 0.80, 0.73, and 0.76 respectively. The AUC values of the two deep learning models were above 0.9. Conclusion: The deep learning model can accurately identify the five subtypes of intracranial hemorrhage and normal images, and it can be used as a new tool for clinical diagnosis in the future.

Download Full-text

Abstract PO-040: Retinal disease diagnosis through an adaptive deep learning model

10.1158/1557-3265.adi21-po-040 ◽

2021 ◽

Author(s):

Haifeng Wang

Keyword(s):

Deep Learning ◽

Retinal Disease ◽

Disease Diagnosis ◽

Learning Model ◽

Deep Learning Model

Download Full-text

Detecting Damage Building Using Real-Time Crowdsourced Images And Transfer Learning

10.21203/rs.3.rs-964756/v1 ◽

2021 ◽

Author(s):

Gaurav Chachra ◽

Qingkai Kong ◽

Jim Huang ◽

Srujay Korlakunta ◽

Jennifer Grannen ◽

...

Keyword(s):

Social Media ◽

Deep Learning ◽

Real Time ◽

Transfer Learning ◽

Learning Model ◽

Research Community ◽

Rescue Work ◽

The Public ◽

Social Media Platforms ◽

Deep Learning Model

Abstract After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged building images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important locations on the images that facilitate the decision.

Download Full-text