Early detection of cancerous tissues in human breast utilizing near field microwave holography

Kumari, Vineeta; Ahmed, Aijaz; Kanumuri, Tirupathiraju; Shakher, Chandra; Sheoran, Gyanendra

doi:10.1002/ima.22384

Cited by 15 publications

(15 citation statements)

References 19 publications

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“…The general equation for the intensity of a recorded hologram is given in Equation () 11 :

I_{δ} = {|ψ_{normalo} + ψ_{normalr} \exp (- jδ)|}^{2}

where I δ is the hologram intensity, Ψ 0 and Ψ r stand for the complex amplitude of object and reference waves respectively, and δ represents the phase shift between object and reference waves. Here, in three‐step phase shifting, the phase of reference wave is shifted sequentially by a phase step of 2π/3.…”

Section: Experimental Investigationmentioning

confidence: 99%

“…10,11 The authors have recently applied the concept of the electronically synthesized reference wave for recording and reconstructing microwave holograms. [11][12][13] Although the setup has small size directive antennas associated with it, whereas other components used for interference were based on waveguides, making the system bulky. The object wave was combined with a known reference wave with constant amplitude and linearly increasing phase shift in each sample, which causes twin image problem.…”

Section: Introductionmentioning

confidence: 99%

“…Further, the introduction of dielectric imaging 9 in holography has opened the area of biomedical applications. The researchers have utilized the concept in detection of cancerous tissues using microwave holography 10,11 . The authors have recently applied the concept of the electronically synthesized reference wave for recording and reconstructing microwave holograms 11‐13 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Miniaturized phase shifting microwave holography: An experimental investigation

Ahmed

Kumari

Shakher

et al. 2021

Int J RF Mic Comp-Aid Eng

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Holographic imaging is a fast and robust technique that has been adapted for near-field applications of microwave imaging. In this work, the experimental development of a miniaturized microwave holographic setup is proposed. This miniaturized setup includes power divider, phase tuner, DC block, and hybrid ring on a single microstrip circuit board. The designed phase tuner component has the ability to tune the phase electronically up to 360 , which makes it suitable to use in the phase-shifting methodology. Moreover, the experimental results for the imaging of metallic objects are shown and compared with the conventional waveguide setup to validate the system's performance. The qualitative results exhibit the effectiveness of utilizing the miniaturized microstrip circuit, making the setup compact and economical. Hence, the proposed structure gives the possibility to make a portable phase-shifting holographic system.

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“…The general equation for the intensity of a recorded hologram is given in Equation () 11 :

I_{δ} = {|ψ_{normalo} + ψ_{normalr} \exp (- jδ)|}^{2}

Section: Experimental Investigationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Miniaturized phase shifting microwave holography: An experimental investigation

Ahmed

Kumari

Shakher

et al. 2021

Int J RF Mic Comp-Aid Eng

Self Cite

View full text Add to dashboard Cite

show abstract

“…The preprocessing method aims to ameliorate the image quality or to improve those image features that are necessary for further processing. 11 Unknown noise, inhomogeneity, poor image contrast, weak borders are the common features of the clinical images. The process of preprocessing can rectify all these problems.…”

Section: Introductionmentioning

confidence: 99%

Early detection of breast malignancy using wavelet features and optimized classifier

Melekoodappattu¹,

Kadan²,

Anoop³

2021

Int J Imaging Syst Tech

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Breast cancer considered to be a significant health issue among women. Early detection will ensure the treatment is easier and more successful. Recently, numerous methodologies have developed using medical imaging to investigate breast cancer. This research seeks to build a computer-aided diagnostic (CAD) system to interpret mammograms. The first stage of CAD includes preprocessing, Fuzzy c means based segmentation applied to a localized area.In the second stage of the CAD method, the extraction of the feature is carried out using three distinct wavelet families with decomposition level at 4 and 6. The ANN, SVM, and ELM classifiers are used in the final stage to enable accurate classification. This article proposes ELM with the Grasshopper Optimization Algorithm (ELM-GOA) to adjust the weight between the input and hidden layer to obtain maximum performance at the middle layer. This method adopts mammogram enhancement, optimum image segmentation, wavelet-based feature extraction, and grasshopper optimization algorithm based ELM to ameliorating the accuracy and reducing the computational cost.The result shows that ELM-GOA has precision and sensitivity of 100% and 98% respectively. The CAD system can identify tumors with 99.33 % accuracy.

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“…Although false positive assumptions can lead to an unwanted biopsy, false negative assumptions can lead to a delayed diagnosis of the real tumor. There is a strong need for measures to identify abnormal tissue automatically in mammograms 2 . Machine‐based mammogram review is now an active area of research that seeks to help radiologists enhance diagnosis by reducing both the number of missing tumors (false negatives) and the number of fine biopsy specimens (false positives).…”

Section: Introductionmentioning

confidence: 99%

Malignancy detection on mammograms by integrating modified convolutional neural network classifier and texture features

Melekoodappattu¹,

Dhas²,

Kumar³

et al. 2021

Int J Imaging Syst Tech

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Breast cancer is detected by identifying malignancy on breast tissue. Emerging technologies in medical image processing are used to interpret histopathology images. For analyzing medical imaging and pathological data, modified deep neural networks are being used. Automatic detection of malignancy is usually achieved in deep learning by capturing features from a convolutional neural network (CNN) and then categorizing them using a fully connected network. A framework to automatically diagnose malignancy using an ensemble approach, including CNN and extraction of image texture features, is implemented in this research. In the CNN phase, the nine‐layer modified CNN is used to classify images. Texture features are derived and their dimension is minimized using maximum variance unfolding to enhance the efficiency of classification in the extraction‐based phase. The results of each phase were then merged to obtain the final decision. The testing specificity and accuracy of our ensemble method on MIAS repository are 98.9% and 99%, respectively and for the DDSM repository are 98.3% and 98.1%. The ensemble approach increases the measurement metrics compared to each phase separately, as per the experimental findings.

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Early detection of cancerous tissues in human breast utilizing near field microwave holography

Cited by 15 publications

References 19 publications

Miniaturized phase shifting microwave holography: An experimental investigation

Miniaturized phase shifting microwave holography: An experimental investigation

Early detection of breast malignancy using wavelet features and optimized classifier

Malignancy detection on mammograms by integrating modified convolutional neural network classifier and texture features

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