320 research outputs found
A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images
Convolutional neural networks (CNNs) have been extensively utilized in medical image
processing to automatically extract meaningful features and classify various medical conditions,
enabling faster and more accurate diagnoses. In this paper, LeNet, a classic CNN architecture,
has been successfully applied to breast cancer data analysis. It demonstrates its ability to extract
discriminative features and classify malignant and benign tumors with high accuracy, thereby
supporting early detection and diagnosis of breast cancer. LeNet with corrected Rectified Linear Unit
(ReLU), a modification of the traditional ReLU activation function, has been found to improve the
performance of LeNet in breast cancer data analysis tasks via addressing the “dying ReLU” problem
and enhancing the discriminative power of the extracted features. This has led to more accurate,
reliable breast cancer detection and diagnosis and improved patient outcomes. Batch normalization
improves the performance and training stability of small and shallow CNN architecture like LeNet.
It helps to mitigate the effects of internal covariate shift, which refers to the change in the distribution
of network activations during training. This classifier will lessen the overfitting problem and reduce
the running time. The designed classifier is evaluated against the benchmarking deep learning
models, proving that this has produced a higher recognition rate. The accuracy of the breast image
recognition rate is 89.91%. This model will achieve better performance in segmentation, feature
extraction, classification, and breast cancer tumor detection
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Tumour grading and discrimination based on class assignment and quantitative texture analysis techniques
Medical imaging represents the utilisation of technology in biology for the purpose of noninvasively revealing the internal structure of the organs of the human body. It is a way to improve the quality of the patient's life through a more precise and rapid diagnosis, and with limited side-effects, leading to an effective overall treatment procedure. The main objective of this thesis is to propose novel tumour discrimination techniques that cover both micro and macro-scale textures encountered in computed tomography (CI') and digital microscopy (DM) modalities, respectively. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and classification. The fractal dimension (FO) as a texture measure was applied to contrast enhanced CT lung tumour images in an aim to improve tumour grading accuracy from conventional CI' modality, and quantitative performance analysis showed an accuracy of 83.30% in distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant tumours. A different approach was adopted for subtype discrimination of brain tumour OM images via a set of statistical and model-based texture analysis algorithms. The combined Gaussian Markov random field and run-length matrix texture measures outperformed all other combinations, achieving an overall class assignment classification accuracy of 92.50%. Also two new histopathological multi resolution approaches based on applying the FO as the best bases selection for discrete wavelet packet transform, and when fused with the Gabor filters' energy output improved the accuracy to 91.25% and 95.00%, respectively. While noise is quite common in all medical imaging modalities, the impact of noise on the applied texture measures was assessed as well. The developed lung and brain texture analysis techniques can improve the physician's ability to detect and analyse pathologies leading for a more reliable diagnosis and treatment of disease
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