Introduction
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To develop a state-of-the-art diagnostic framework for automated, precise evaluation of auriculotemporal and ossicular disorders based on the HRSCT-DLT model, improving diagnostic accuracy and clinical insight in otolaryngology.
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To automate crucial diagnostic activities such as ossicle segmentation, fracture detection, and disruption cause categorization using the CNN-UNet deep learning model within the HRSCT-DLT framework for improved efficiency and accuracy in diagnosis.
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To assess the HRSCT-DLT model’s clinical effects, validate the framework’s efficacy, and pave the way for future research and advancements, this will serve as a standard for successfully incorporating cutting-edge technology into medical diagnosis.
Literature survey
Segmentation of CT Scans of the Temporal Bone
Deep learning in ear disease diagnosis
Diagnostic tools and techniques
Perspectives from research on otosclerosis and dentistry
Author | Method | Application | Limitation |
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Neves et al. [21] | CNN-based automated system for segmenting CT scans | Automatic temporal bone CT segmentation using CNN. The models learned to segment the cochlea, its ossification facial nerve, and sigmoid sinus. | Error and misclassification analysis, as well as the creation of intuitive user interfaces, still have space for development. |
Li et al. [22] | 3D-DSD Net | The highly connected network uses 3D multi-pooling feature fusion. Dice factor, precision, sensibility, and Hausdorff distance evaluate efficacy. | 3D-DSD Net’s generalizability, clinical integration, and error analysis need more research. |
Fujima et al. [24] | DL analysis for identifying otosclerosis | DL systems like AlexNet, and ResNet to evaluate their examination data and develop a diagnostic model. | Need more attention on in generalizability, error assessment, clinical impact, and data variety. |
Ke et al. [23] | CNN based auto segmentation | The impetus was automatic temporal bone CT segmentation in adults and children. | Maintain limits in real-time clinical circumstances, produce accurate predictions, and expand the dataset to include individuals with more characteristics and diseases. |
Wang et al. [25] | Diagnose persistent middle ear diseases using DL. | MESIC used a “region of interest” (ROI) area search network and a classification network to provide reliable diagnoses. | Not efficient and trustworthy |
Khan et al. [26] | CNN-Medical imaging | An innovative use of CNNs, including state-of-the-art models like DenseNet, to automatically identify TM and ME infections in medical imaging. | Need attention on clinical validity, data diversity, and interpretability |
Erolu et al. [27] | AI –CT scans | AI modeling was utilized to determine if CT scans of chronic otitis media (COM) patients could distinguish cholesteatoma from non-cholesteatoma. | Need AI for correct diagnosis Acute cholesteatoma |
Duan et al. [28] | DL-PCD screening | Deep learning’s capacity to distinguish OME-related otitis media (OM) from PCD-related OM. | Limited accuracy and reliability |
Jeevakala et al. [29] | Automated method for IAC location and nerve separation | The Mask R-CNN and U-Net-powered approach located and segmented the IAC and nerves, studies indicated. | Computational complexity |
Propoced system model
Data collection and preparation
Data annotation
Data preprocessing
CNN-UNet model development
Convolutional neural network model
CNN-UNet algorithm in HRSCT-DLT framework
Algorithm 1: HRSCT-DLT Model |
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Begin |
Step 1: Define hyperparameters input_shape = (img_height, img_width, img_channels) # Define image dimensions n_classes = num_classes # Define the number of segmentation classes learning_rate = 0.001 batch_size = 32 epochs = 50 Step 2: Define a function to build the CNN-UNet model function build_cnn_unet(input_shape, n_classes) inputs = Input(input_shape) # Encoding Path conv1 = Convl2D(64, 3, activation=’relu’, padding=’same’)(inputs) conv1 = Convl2D(64, 3, activation=’relu’, padding=’same’)(conv1) pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) # Including more encoding layers # Decoding Path up6 = UpSampling2D(size=(2, 2))(conv6) up6 = Convl2D(64, 2, activation=’relu’, padding=’same’)(up6) merge6 = Concatenate(axis = 3)([conv3, up6]) conv6 = Convl2D(64, 3, activation=’relu’, padding=’same’)(merge6) conv6 = Convl2D(64, 3, activation=’relu’, padding=’same’)(conv6) # Including more decoding layers… # Output Layer out = Convl2D(n_classes, 1, activation=’softmax’)(conv10) return Model(inputs = inputs, outputs = out) Step 3: Load and preprocess your dataset X_train, Y_train = load_and_preprocess_data(data_path) X_train, X_val, Y_train, Y_val = split_train_and_validation_data(X_train, Y_train, validation_ratio) Step 4: Build and compile the model model = build_cnn_unet(input_shape, n_classes) model.compile(optimizer = Adam(learning_rate), loss=’categorical_crossentropy’, metrics=[‘accuracy’]) Step 5: Train the model and save model.fit(X_train, Y_train, batch_size = batch_size, epochs = epochs, validation_data=(X_val, Y_val)) model.save(‘HRSCT-DLT_model.h5’) Step 6: Perform segmentation on the test dataset function segment_new_images(new_images, model) predictions = model.predict(new_images) return predictions Step 7: End |
Experimental results and analysis
Setup
Results
Methods | Precision | Recall | F1-Score | Dice Coefficient | IoU | Diagnostic Accuracy |
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CNN-GoogLeNet | 86.52 | 85.23 | 86.45 | 0.8724 | 0.8893 | 0.7496 |
CNN-DenseNet | 89.41 | 88.47 | 89.27 | 0.9006 | 0.9024 | 0.7951 |
CNN-ResNet | 92.56 | 93.64 | 92.98 | 0.9421 | 0.9451 | 0.8463 |
Mask R-CNN-UNet | 94.27 | 95.47 | 94.91 | 0.9674 | 0.9689 | 0.8749 |
HRSCT-DT | 98.01 | 98.97 | 99.12 | 0.9897 | 0.9924 | 0.9624 |