Executing Spark BigDL for Leukemia Detection from Microscopic Images using Transfer Learning

Aftab, Muhammad Omer; Awan, Mazhar Javed; Khalid, Shahid; Javed, Rabia; Shabir, Hassan

doi:10.1109/caida51941.2021.9425264

Cited by 48 publications

(12 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Research material [ 15 ] was applied to a vision transformer, but the proposed model was evaluated with explainable AI, which is more reliable. The groundwork [ 16 ] chose the BigDL method, but any evaluation matrix was missing. The proposed method has more contributing factors to a reliable and effective model in these cases.…”

Section: Results and Analysismentioning

confidence: 99%

“…The ViT-CNN ensemble model extracts features from cell pictures in two alternative ways to improve classification, resulting in a very accurate detection method with 99.03% accuracy. Aftab et al have proposed [ 16 ] a methodology for detecting leukemia using the Apache Spark BigDL library and CNN architecture for GoogLeNet deep transfer learning. They used microscopic images of human blood cells and reached a 96.42% accuracy rate.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

Abir

Uddin

Khanam

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

White blood cells (WBCs) are blood cells that fight infections and diseases as a part of the immune system. They are also known as “defender cells.” But the imbalance in the number of WBCs in the blood can be hazardous. Leukemia is the most common blood cancer caused by an overabundance of WBCs in the immune system. Acute lymphocytic leukemia (ALL) usually occurs when the bone marrow creates many immature WBCs that destroy healthy cells. People of all ages, including children and adolescents, can be affected by ALL. The rapid proliferation of atypical lymphocyte cells can cause a reduction in new blood cells and increase the chances of death in patients. Therefore, early and precise cancer detection can help with better therapy and a higher survival probability in the case of leukemia. However, diagnosing ALL is time-consuming and complicated, and manual analysis is expensive, with subjective and error-prone outcomes. Thus, detecting normal and malignant cells reliably and accurately is crucial. For this reason, automatic detection using computer-aided diagnostic models can help doctors effectively detect early leukemia. The entire approach may be automated using image processing techniques, reducing physicians’ workload and increasing diagnosis accuracy. The impact of deep learning (DL) on medical research has recently proven quite beneficial, offering new avenues and possibilities in the healthcare domain for diagnostic techniques. However, to make that happen soon in DL, the entire community must overcome the explainability limit. Because of the black box operation’s shortcomings in artificial intelligence (AI) models’ decisions, there is a lack of liability and trust in the outcomes. But explainable artificial intelligence (XAI) can solve this problem by interpreting the predictions of AI systems. This study emphasizes leukemia, specifically ALL. The proposed strategy recognizes acute lymphoblastic leukemia as an automated procedure that applies different transfer learning models to classify ALL. Hence, using local interpretable model-agnostic explanations (LIME) to assure validity and reliability, this method also explains the cause of a specific classification. The proposed method achieved 98.38% accuracy with the InceptionV3 model. Experimental results were found between different transfer learning methods, including ResNet101V2, VGG19, and InceptionResNetV2, later verified with the LIME algorithm for XAI, where the proposed method performed the best. The obtained results and their reliability demonstrate that it can be preferred in identifying ALL, which will assist medical examiners.

show abstract

Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

Abir

Uddin

Khanam

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Recently, using different deep learning approaches, tremendous advances have been achieved in the field of automated voice recognition (ASR) [22][23][24]. In this section, a complete comparison of cutting-edge strategies currently being employed with a specific emphasis on the various deep learning methods.…”

Section: Related Workmentioning

confidence: 99%

Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System

Ali¹,

Jaber

Khalil

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Automatic speech recognition (ASR) is an effective technique that can convert human speech into text format or computer actions. ASR systems are widely used in smart appliances, smart homes, and biometric systems. Signal processing and machine learning techniques are incorporated to recognize speech. However, traditional systems have low performance due to a noisy environment. In addition to this, accents and local differences negatively affect the ASR system’s performance while analyzing speech signals. A precise speech recognition system was developed to improve the system performance to overcome these issues. This paper uses speech information from jim-schwoebel voice datasets processed by Mel-frequency cepstral coefficients (MFCCs). The MFCC algorithm extracts the valuable features that are used to recognize speech. Here, a sparse auto-encoder (SAE) neural network is used to classify the model, and the hidden Markov model (HMM) is used to decide on the speech recognition. The network performance is optimized by applying the Harris Hawks optimization (HHO) algorithm to fine-tune the network parameter. The fine-tuned network can effectively recognize speech in a noisy environment.

show abstract

“…Acute lymphoblastic leukemia (ALL) is one of the leukemia types [3]. ALL is a form of leukemia that originates in the bone marrow and affects the maturation of B and T lymphocyte cells [4].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for the Detection of Acute Lymphoblastic Leukemia Subtypes on Microscopic Images: A Systematic Literature Review

2023

View full text Add to dashboard Cite

Computer vision research in detecting and classifying the subtype Acute Lymphoblastic Leukemia (ALL) has contributed to computer-aided diagnosis with improved accuracy. Another contribution is to serve as an assistant and second opinion for doctors and hematologists in diagnosing the ALL subtype.Early detection can also rely on computer-aided diagnosis to determine initial treatment. The purpose of this study is to review the progress of research in the detection and classification of ALL subtypes. The method's discussion focuses on the application of deep learning to the domain of object detection and classification. Motivations, challenges, and future research recommendations are thoroughly discussed to improve understanding and progress in this field of study. The study was carried out methodically by analyzing a collection of papers on the detection and classification of ALL subtypes published in science direct, IEEE, and PubMed from 2018 to 2022. The analysis of this paper field is included in the results of the selected paper. The paper selection from among 65 papers was based on inclusion and exclusion methods. Based on research methods and objectives, papers are divided into two large groups. The first group discusses the classification of ALL subtypes, while the second group discusses the detection of ALL subtypes. The discussion of prior research reveals some challenging issues and future work, such as the limited availability of the ALL subtypes dataset, the high computational complexity of the deep learning model, and further exploration of transformers in computer vision as a reference for research gaps that can contribute to future research.

show abstract

Executing Spark BigDL for Leukemia Detection from Microscopic Images using Transfer Learning

Cited by 48 publications

References 24 publications

Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System

Deep Learning for the Detection of Acute Lymphoblastic Leukemia Subtypes on Microscopic Images: A Systematic Literature Review

Contact Info

Product

Resources

About