An Improved Strategy for Predicting Diagnosis, Survivability, and Recurrence of Breast Cancer

Self Cite

227

116

Brain tumor is one of the most dreadful natures of cancer and caused a huge number of deaths among kids and adults from the past few years. According to WHO standard, the 700,000 humans are being with a brain tumor and around 86,000 are diagnosed since 2019. While the total number of deaths due to brain tumors is 16,830 since 2019 and the average survival rate is 35%. Therefore, automated techniques are needed to grade brain tumors precisely from MRI scans. In this work, a new deep learning-based method is proposed for microscopic brain tumor detection and tumor type classification. A 3D convolutional neural network (CNN) architecture is designed at the first step to extract brain tumor and extracted tumors are passed to a pretrained CNN model for feature extraction. The extracted features are transferred to the correlation-based selection method and as the output, the best features are selected. These selected features are validated through feed-forward neural network for final classification. Three BraTS datasets 2015, 2017, and 2018 are utilized for experiments, validation, and accomplished an accuracy of 98.32, 96.97, and 92.67%, respectively. A comparison with existing techniques shows the proposed design yields comparable accuracy.

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture

Rehman

Khan

Mehmood

et al. 2020

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227

116

“…Normally preprocessing is employed to improve and enhance the input data to smooth line further processing (Lung, Salam, Rehman, Rahim, & Saba, 2014; Majid et al, 2020; Marie‐Sainte, Aburahmah, Almohaini, & Saba, 2019; Marie‐Sainte, Saba, et al, 2019; Rehman, Khan, Mehmood, et al, 2020). In this case, it was mandatory since the MRI images were acquired from various modalities that involve artifacts.…”

Section: Proposed Model For Tumor Detection and Classificationmentioning

confidence: 99%

“…Analysis and classification of medical imaging play an important role in detecting abnormalities in various body organs, such as blood cancer (Abbas, Saba, Mohamad, et al, 2018; Abbas, Saba, Rehman, et al, 2019; Abbas, Saba, Rehman, et al, 2019; Abbas, Saba, Mehmood, et al, 2019; Rehman, Abbas, Saba, Mahmood, & Kolivand, 2018; Rehman, Abbas, Saba, Rahman, et al, 2018; Rehman, Abbas, Saba, Mehmood, et al, 2018), lung cancer (Khan, Nazir, et al, 2019; Saba, 2019; Saba, 2020; Saba, Sameh, Khan, Shad, & Sharif, 2019), brain tumor (Saba, Mohamed, El‐Affendi, Amin, & Sharif, 2020), breast cancer (Marie‐Sainte, Saba, Alsaleh, Alotaibi, & Bin, 2019; Mughal, Muhammad, Sharif, Rehman, & Saba, 2018; Mughal, Muhammad, Sharif, Saba, & Rehman, 2017; Mughal, Sharif, Muhammad, & Saba, 2018). Moreover, organ abnormalities often lead to rapid growth of tumors, which is the world's leading cause of death (Fahad, Khan, Saba, Rehman, & Iqbal, 2018; Saba, Al‐Zahrani, & Rehman, 2012; Saba, Bokhari, Sharif, Yasmin, & Raza, 2018; Saba, Rehman, Mehmood, Kolivand, & Sharif, 2018; Ullah et al, 2019; Yousaf, Mehmood, Saba, et al, 2019; Yousaf, Mehmood, Awan, et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Brain tumor detection and multi‐classification using advanced deep learning techniques

Sadad

Rehman

Munir

et al. 2021

Self Cite

146

A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and patients' survival rate. There are distinct forms, properties, and therapies of brain tumors. Therefore, manual brain tumor detection is complicated, time-consuming, and vulnerable to error. Hence, automated computer-assisted diagnosis at high precision is currently in demand. This article presents segmentation through Unet architecture with ResNet50 as a backbone on the Figshare data set and achieved a level of 0.9504 of the intersection over union (IoU). The preprocessing and data augmentation concept were introduced to enhance the classification rate. The multi-classification of brain tumors is performed using evolutionary algorithms and reinforcement learning through transfer learning. Other deep learning methods such as ResNet50, DenseNet201, MobileNet V2, and InceptionV3 are also applied. Results thus obtained exhibited that the proposed research framework performed better than reported in state of the art. Different CNN, models applied for tumor classification such as MobileNet V2, Inception V3, ResNet50, Den-seNet201, NASNet and attained accuracy 91.8, 92.8, 92.9, 93.1, 99.6%, respectively. However, NASNet exhibited the highest accuracy.Two processes of transfer learning: freeze and fine-tune, are performed to extract significant features from MRI slices. Brain tumor multi-classification is performed using transfer learning, ResNet50-UNet, and NASNet architecture.

“…Medical imaging classification plays a substantial role to identify irregularity in different organs of the body, such as blood cancer (Abbas et al, 2018, 2018a, 2018b, 2018c; Rehman, Abbas, Saba, Mahmood, & Kolivand, 2018; Rehman, Abbas, Saba, Mehmood, Mahmood, et al, 2018; Rehman, Abbas, Saba, Rahman, Mehmood, et al, 2018), lung cancer (Khan, Nazir, et al, 2019; Saba, Khan, Islam, et al, 2019; Saba, Khan, Rehman, et al, 2019), brain tumor (Khan, Lali, et al, 2019; Rehman, Khan, Saba, et al, 2021), breast cancer (Mughal, Muhammad, Sharif, Saba, & Rehman, 2017; Mughal, Muhammad, Sharif, Rehman, & Saba, 2018; Marie‐Sainte, Saba, et al, 2019; Saba, Sameh, Khan, Shad, & Sharif, 2019), stomach cancer (Khan, Javed, Sharif, Saba, & Rehman, 2019, Khan, Sharif, et al, 2019), skin cancer (Javed, Rahim, Saba, & Rashid, 2019; Javed, Rahim, & Saba, 2019; Javed, Rahim, Saba, & Rehman, 2020; Javed, Saba, Shafry, & Rahim, 2020; Khan, Akram, et al, 2019; Khan, Javed, et al, 2019; Khan, Sharif, et al, 2019; Saba, Khan, Rehman, et al, 2019), Retinal image analysis (Jamal, Hazim Alkawaz, Rehman, & Saba, 2017; Saba, Bokhari, Sharif, Yasmin, & Raza, 2018; Ullah et al, 2019) and so on. The abnormality of the organ often results in rapid tumor development, which is the primary cause of death worldwide (Fahad, Khan, Saba, Rehman, & Iqbal, 2018; Rahim, Norouzi, Rehman, & Saba, 2017; Rahim, Rehman, Kurniawan, & Saba, 2017; Saba, Rehman, Mehmood, Kolivand, & Sharif, 2018; Saba, Bokhari, Sharif, Yasmin, & Raza, 2018; Saba, Al‐Zahrani, & Rehman, 2012; Ullah et al, 2019; Yousaf, Mehmood, Saba, et al, 2019; Yousaf, Mehmood, Awan, et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Brain tumor segmentation using K‐means clustering and deep learning with synthetic data augmentation for classification

Khan

Harouni

et al. 2021

159

Image processing plays a major role in neurologists' clinical diagnosis in the medical field. Several types of imagery are used for diagnostics, tumor segmentation, and classification. Magnetic resonance imaging (MRI) is favored among all modalities due to its noninvasive nature and better representation of internal tumor information. Indeed, early diagnosis may increase the chances of being lifesaving. However, the manual dissection and classification of brain tumors based on MRI is vulnerable to error, time‐consuming, and formidable task. Consequently, this article presents a deep learning approach to classify brain tumors using an MRI data analysis to assist practitioners. The recommended method comprises three main phases: preprocessing, brain tumor segmentation using k‐means clustering, and finally, classify tumors into their respective categories (benign/malignant) using MRI data through a finetuned VGG19 (i.e., 19 layered Visual Geometric Group) model. Moreover, for better classification accuracy, the synthetic data augmentation concept i s introduced to increase available data size for classifier training. The proposed approach was evaluated on BraTS 2015 benchmarks data sets through rigorous experiments. The results endorse the effectiveness of the proposed strategy and it achieved better accuracy compared to the previously reported state of the art techniques.