Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Дек. 18, 2024

Recognition and segmentation of brain tumours (BT) using MR images are valuable tedious processes in the healthcare industry. Earlier diagnosis localization BT provide timely options to select effective treatment plans for doctors can save lives. from Magnetic Resonance Images (MRI) is considered a big challenge owing difficulty tissues, segmenting them healthier tissue challenging when manual done through radiologists. Among recent proposals method, method based on machine learning (ML) image processing could be better. Thus, DL-based extensively applied, convolutional network has better effects. The deep model problem large loss information number parameters encoding decoding processes. With this motivation, article presents new Deep Transfer Learning with Semantic Segmentation Medical Image Analysis (DTLSS-MIA) technique MRI images. DTLSS-MIA aims segment affected area At first, presented utilizes Median filtering (MF) approach optimize quality remove noise. For semantic follows DeepLabv3 + backbone EfficientNet determining region. Moreover, CapsNet architecture employed feature extraction process. Lastly, crayfish optimization (CFO) diffusion variational autoencoder (D-VAE) used as classification mechanism, CFO effectively tunes D-VAE hyperparameter. simulation analysis validated benchmark dataset. performance validation exhibited superior accuracy value 99.53% over other methods.

Язык: Английский