Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

Rehman, Amjad; Khan, Muhammad Attique; Mehmood, Zahid; Sardaraz, Muhammad; Rashid, Muhammad

doi:10.1002/jemt.23429

Cited by 85 publications

(50 citation statements)

References 62 publications

(89 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Improving the graphic features of an image is the primary objective of contrast enhancement. It is a preprocessing step that is used in many applications like biomedical imaging, agriculture infections diagnosis, and some others [ 37 , 38 , 39 , 40 , 41 , 42 ]. The impact of low contrast images is not useful for feature extraction, as visually, tumors are not visible and error prone.…”

Section: Proposed Methodologymentioning

confidence: 99%

Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists

et al. 2020

Self Cite

View full text Add to dashboard Cite

Manual identification of brain tumors is an error-prone and tedious process for radiologists; therefore, it is crucial to adopt an automated system. The binary classification process, such as malignant or benign is relatively trivial; whereas, the multimodal brain tumors classification (T1, T2, T1CE, and Flair) is a challenging task for radiologists. Here, we present an automated multimodal classification method using deep learning for brain tumor type classification. The proposed method consists of five core steps. In the first step, the linear contrast stretching is employed using edge-based histogram equalization and discrete cosine transform (DCT). In the second step, deep learning feature extraction is performed. By utilizing transfer learning, two pre-trained convolutional neural network (CNN) models, namely VGG16 and VGG19, were used for feature extraction. In the third step, a correntropy-based joint learning approach was implemented along with the extreme learning machine (ELM) for the selection of best features. In the fourth step, the partial least square (PLS)-based robust covariant features were fused in one matrix. The combined matrix was fed to ELM for final classification. The proposed method was validated on the BraTS datasets and an accuracy of 97.8%, 96.9%, 92.5% for BraTs2015, BraTs2017, and BraTs2018, respectively, was achieved.

show abstract

Section: Proposed Methodologymentioning

confidence: 99%

Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…From the past few years, features extractions gain much attention in the area of pattern recognition based on several applications such as medical (Rehman et al, 2020), surveillance (M. Sharif, Akram, Raza, Saba, & Rehman, 2020a), agriculture (Adeel et al, 2019; Safdar et al, 2019), and many more (M. A. Khan, Javed, Sharif, Saba, & Rehman, 2019b; S. A. Khan et al, 2019). The main purpose of feature extraction is to analyze the patterns into a relevant category based on a few strong points.…”

Section: Proposed Methodologymentioning

confidence: 99%

Automated design for recognition of blood cells diseases from hematopathology using classical features selection and ELM

Khan

Qasim

Lodhi

et al. 2020

Microscopy Res & Technique

View full text Add to dashboard Cite

In the human immune system, the white blood cells (WBC) creates bone and lymphoid masses. These cells defend the human body toward several infections, such as fungi and bacteria. The popular WBC types are Eosinophils, Lymphocytes, Neutrophils, and Monocytes, which are manually diagnosis by the experts. The manual diagnosis process is complicated and time‐consuming; therefore, an automated system is required to classify these WBC. In this article, a new method is presented for WBC classification using feature selection and extreme learning machine (ELM). At the very first step, data augmentation is performed to increases the number of images and then implement a new contrast stretching technique name pixel stretch (PS). In the next step, color and gray level size zone matrix (GLSZM) features are calculated from PS images and fused in one vector based on the level of high similarity. However, few redundant features are also included that affect the classification performance. For handling this problem, a maximum relevance probability (MRP) based feature selection technique is implemented. The best‐selected features computed from a fitness function are ELM in this work. All maximum relevance features are put to ELM, and this process is continued until the error rate is minimized. In the end, the final selected features are classified through Cubic SVM. For validation of the proposed method, LISC and Dhruv datasets are used, and it achieved the highest accuracy of 96.60%. From the results, it is clearly shown that the proposed method results are improved as compared to other implemented techniques.

show abstract

“…However, the approximated number of deaths due to brain tumor is 16,830 since 2019 and the average survival rate is 35% (NBTC, 2019). Due to the considerable number of deaths, nowadays, it is the hottest research topic in medical imaging (Mittal et al, 2020; Rehman et al, 2020). However, the early identification of tumors could increase the human survival rate by a malignant tumor (Khan et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…For the treatment of tumors, physicians use several options such as chemotherapy, radiotherapy, and surgery, however; it always depends on the size, shape, and nature of a tumor (Ejaz et al, 2018; Rehman et al, 2020). The clinical technology like MRI yields detailed information of healthy and tumor regions in the forms of their slices (Amin et al, 2019; Amin, Sharif, Yasmin, & Raza, 2019; Ramzan, Khan, Iqbal, Saba, & Rehman, 2020; Tahir et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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

Rehman

Khan

Mehmood

et al. 2020

Microscopy Res & Technique

Self Cite

225

116

View full text Add to dashboard Cite

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.

show abstract

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

Cited by 85 publications

References 62 publications

Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists

Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists

Automated design for recognition of blood cells diseases from hematopathology using classical features selection and ELM

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

Contact Info

Product

Resources

About