Enhanced lung image segmentation using deep learning

Abstract

With the advances in technology, assistive medical systems are emerging with rapid growth and helping healthcare professionals. The proactive diagnosis of diseases with artificial intelligence (AI) and its aligned technologies has been an exciting research area in the last decade. Doctors usually detect tuberculosis (TB) by checking the lungs’ X-rays. Classification using deep learning algorithms is successfully able to achieve accuracy almost similar to a doctor in detecting TB. It is found that the probability of detecting TB increases if classification algorithms are implemented on segmented lungs instead of the whole X-ray. The paper’s novelty lies in detailed analysis and discussion of U-Net + + results and implementation of U-Net + + in lung segmentation using X-ray. A thorough comparison of U-Net + + with three other benchmark segmentation architectures and segmentation in diagnosing TB or other pulmonary lung diseases is also made in this paper. To the best of our knowledge, no prior research tried to implement U-Net + + for lung segmentation. Most of the papers did not even use segmentation before classification, which causes data leakage. Very few used segmentations before classification, but they only used U-Net, which U-Net + + can easily replace because accuracy and mean_iou of U-Net + + are greater than U-Net accuracy and mean_iou , discussed in results, which can minimize data leakage. The authors achieved more than 98% lung segmentation accuracy and mean_iou 0.95 using U-Net + + , and the efficacy of such comparative analysis is validated.

A Self‐Configuring Deep Learning Network for Segmentation of Temporal Bone Anatomy in Cone‐Beam CT Imaging

“…Due to the prohibitive disadvantages of atlas-based segmentation and the increase in computational power of modern computers, there has been rising interest in training deep learning networks to segment temporal bone anatomy. [14][15][16][17][18][19][20][21] Deep learning networks for medical imaging segmentation are extensively used in orthopedics, [22][23][24] neurosurgery, [25][26][27][28] surgical oncology, [29][30][31] and other surgical specialties 32,33 but are relatively scarce in otology/neurotology. We, therefore, present an end-toend deep learning model that accurately segments temporal bone structures with potential applications for surgical planning, surgical skills training, and image guidance.…”

Analysis of lung scan imaging using deep multi‐task learning structure for Covid‐19 disease

“…Recently, Gite et al. [19] proposed the Unet ++ structure for dividing the lungs with X‐rays and found that the likelihood of TB detection increases if classification algorithms are applied to segmented lungs instead of the entire X‐ray. The comparison of U‐Net ++ with three other classification architectures in diagnosing tuberculosis or other pulmonary diseases indicated the superiority of the proposed approach.…”

A hybrid CNN-Random Forest algorithm for bacterial spore segmentation and classification in TEM images

“…The convolution operation is performed between the input data and a set of filters, also known as kernels or weights, that are learned in the training process. The filters slide across the input, computing a dot product between the filter and the input data [28]. The number of filters determines the number of feature maps that are generated by the convolutional layer, each capturing different features or aspects of the input data, which for this work are 2D TEM images.…”