Medical Image Captioning Using Optimized Deep Learning Model.

Medical image captioning provides the visual information of medical images in the form of natural language. It requires an efficient approach to understand and evaluate the similarity between visual and textual elements and to generate a sequence of output words. A novel show, attend, and tell model (ATM) is implemented, which considers a visual attention approach using an encoder-decoder model. But the show, attend, and tell model is sensitive to its initial parameters. Therefore, a Strength Pareto Evolutionary Algorithm-II (SPEA-II) is utilized to optimize the initial parameters of the ATM. Finally, experiments are considered using the benchmark data sets and competitive medical image captioning techniques. Performance analysis shows that the SPEA-II-based ATM performs significantly better as compared to the existing models.

Effectivity of combined diffusion-weighted imaging and contrast-enhanced MRI in malignant and benign breast lesions.

PURPOSE: Mammography is the most commonly used diagnostic test for breast lesion detection and evaluation, but in dense breast parenchyma it lowers its sensitivity to detect small lesions. Sensitivity and specificity improves with combined use of contrast-enhanced magnetic resonance imaging (CE-MRI) and diffusion-weighted imaging (DWI) in differentiating benign and malignant breast lesions. The aim of the study was to evaluate the effectivity of combined dynamic CE-MRI and DWI in differentiating benign and malignant lesions, and to calculate the apparent diffusion coefficient (ADC) values of malignant and benign lesions of the breast. MATERIAL AND METHODS: Fifty-seven patients with 68 lesions were included in the study. MRI of breast using different sequences was acquired on 1.5 Tesla Machine with dedicated breast coils. Dynamic CE-MRI along with DWI was acquired for each patient. Histopathological reports were accepted as the standard of reference. RESULTS: Out of 68 lesions, 37 were malignant on biopsy (54.4%) and 31 were benign (45.5%). The sensitivity of CE-MRI was 92%, specificity 84.21%, positive predictive values (PPV) 88.46 %, and negative predictive values (NPP) 88.89%. The sensitivity of DWI-MRI was 91.6%, specificity was 90.6%, PPV 91.6%, and NPP 90.6%. The sensitivity of combined DWI-MRI and CE-MRI was 95.0%, specificity was 96.43%, PPV 97.44%, and NPP 93.10%. Mean ADCs of benign lesions (b = 800) was 1.905 ± 0.59 × 10-3 mm (2)/s, which was significantly higher than those of malignant lesions (b = 800) 1.014 ± 0.47 × 10-3 mm (2)/s. CONCLUSION: Multi-parametric MRI is an excellent non-invasive modality with high sensitivity and specificity to differentiate malignant from benign breast lesions.

Plexiform Neurofibroma in the Axilla with Intraosseous Neurofibroma in the Humerus in Neurofibromatosis 5.

Segmental neurofibromatosis type 5 (NF5) is a rare form of NF, in which the cutaneous and/or neural changes are confined to one region of the body. It is often underdiagnosed or undetected due to the absence of symptoms. Inheritance of NF is different from other types of NF. Intraosseous neurofibromas are rare tumors which develop from nerves supplying the periosteum or nerves following the intraosseous course. Plexiform neurofibroma with associated intraosseous neurofibroma is extremely rare. We report a case of plexiform neurofibroma in the left axilla of a 17-year-old female with associated intraosseous neurofibroma in adjoining left humerus.

Internal Jugular Vein Thrombosis in Isolated Tuberculous Cervical Lymphadenopathy.

Tuberculosis is a common infectious disease with a high prevalence in developing countries and presents a major public health issue. Internal jugular vein (IJV) thrombosis is a rare complication in tuberculous cervical lymphadenopathy. We report a case of 26-year male patient with a history of low-grade evening rise in fever, dry cough, loss of appetite, and loss of weight with swelling in lower neck on right side. Ultrasonography (USG) neck showed well-defined hypoechoic lymph nodes posterior to right IJV and common carotid artery in the lower neck at level IV and in the right supraclavicular region showing central necrotic areas with adjoining IJV thrombosis. The association between tuberculosis and deep vein thrombosis is rare. Awareness of IJV thrombosis in isolated cervical lymphadenopathy needs high diagnostic suspicion and prompt treatment to avoid fatal complication. Our case is rare as there was isolated tuberculous cervical lymphadenopathy with adjoining IJV thrombosis. Both USG and computed tomography (CT) are accurate and reliable radiological investigations for detecting IJV thrombosis along with cervical lymph nodes. They are useful in assessing surrounding soft tissue and fat planes and knowing the size and extent of cervical lymphadenopathy. USG is inexpensive and readily available for monitoring response to treatment.